Introduction
The AI revolution is no longer a distant future—it’s reshaping industries today. By 2025, the global AI market is projected to reach $190 billion (Statista, 2023), with generative AI tools like ChatGPT and Midjourney contributing an estimated $4.4 trillion annually to the global economy (McKinsey, 2023). For tech professionals and organizations, this rapid evolution presents unparalleled opportunities but also demands strategic navigation. As an AI expert with a decade of experience working at Big Tech companies and scaling AI-first startups, I’ve witnessed firsthand the transformative power of well-executed AI strategies. This blog post distills actionable insights for:
Let’s explore how to turn AI’s potential into measurable results. Breaking into AI – A Blueprint for Early-Career Professionals The Skills That Matter in 2024 The AI job market is evolving beyond traditional coding expertise. While proficiency in Python and TensorFlow remains valuable, employers now prioritize three critical competencies: 1. Prompt Engineering: With generative AI tools like GPT4/o/o1-/o-3, Deepseek-R1, Claude Sonnet 3.5 etc., the ability to craft precise prompts is becoming a baseline skill. For example, a marketing analyst might use prompts like, “Generate 10 customer personas for a fintech app targeting Gen Z, including pain points and preferred channels.” 2. AI Literacy: 85% of hiring managers now require familiarity with responsible AI frameworks ([Deloitte, 2023](https://www2.deloitte.com)). This includes understanding bias mitigation and compliance with regulations like the EU AI Act. 3. Cross-Functional Collaboration: AI projects fail when technical teams operate in silos. Professionals who can translate business goals into technical requirements—and vice versa—are indispensable. Actionable Steps to Launch Your AI Career 1. Develop a "T-shaped" Skill Profile: Deepen expertise in machine learning (the vertical bar of the “T”) while broadening knowledge of business applications. For instance, learn how recommendation systems impact e-commerce conversion rates. 2. Build an AI Portfolio: Document projects that solve real-world problems. A compelling example: fine-tuning Meta’s Llama 2 model to summarize legal contracts, then deploying it via Hugging Face’s Inference API. 3. Leverage Micro-Credentials: Google’s [Generative AI Learning Path](https://cloud.google.com/blog/topics/training-certifications/new-generative-ai-training) and DeepLearning.AI’s short courses provide industry-recognized certifications that demonstrate proactive learning. From Individual Contributor to AI Leader – Strategies for Mid/Senior Professionals The Four Pillars of Effective AI Leadership Transitioning from technical execution to strategic leadership requires mastering these core areas: 1. Strategic Vision Alignment: Successful AI initiatives directly tie to organizational objectives. For example, a retail company might set the OKR: “Reduce supply chain forecasting errors by 40% using time-series AI models by Q3 2024.” 2. Risk Mitigation Frameworks: Generative AI models like GPT-4 can hallucinate inaccurate outputs. Leaders implement guardrails such as IBM’s [AI Ethics Toolkit](https://www.ibm.com), which includes bias detection algorithms and human-in-the-loop validation processes. 3. Stakeholder Buy-In: Use RACI matrices (Responsible, Accountable, Consulted, Informed) to clarify roles. For instance, when deploying a customer service chatbot, legal teams must be “Consulted” on compliance, while CX leads are “Accountable” for user satisfaction metrics. 4. ROI Measurement: Track metrics like inference latency (time to generate predictions) and model drift (performance degradation over time). One fintech client achieved a 41% improvement in fraud detection accuracy by combining XGBoost with transformer models, while reducing false positives by 22%. Building an AI-First Organization – A Playbook for Startups The AI Strategy Canvas 1. Problem Identification: Focus on high-impact “hair-on-fire” pain points. A logistics startup automated customs documentation—a manual 6-hour process—into a 2-minute task using GPT-4 and OCR. 2. Tool Selection Matrix: Compare open-source (e.g., Hugging Face’s LLMs) vs. enterprise solutions (Azure OpenAI). Key factors: data privacy requirements, scalability, and in-house technical maturity. 3. Implementation Phases: - Pilot (1-3 Months): Test viability with an 80/20 prototype. Example: A SaaS company used a low-code platform to build a churn prediction model with 82% accuracy using historical CRM data. - Scale (6-12 Months): Integrate models into CI/CD pipelines. One e-commerce client reduced deployment time from 14 days to 4 hours using AWS SageMaker. - Optimize (Ongoing): Conduct A/B tests between model versions. A/B testing showed that a hybrid CNN/Transformer model improved image recognition accuracy by 19% over pure CNN architectures. Generative AI in Action – Enterprise Case Studies Use Case 1: HR Transformation at a Fortune 500 Company Challenge: 45-day hiring cycles caused top candidates to accept competing offers. Solution: - GPT-4 drafted job descriptions optimized for DEI compliance - LangChain automated interview scoring using rubric-based grading - Custom embeddings matched candidates to team culture profiles Result: 33% faster hiring, 28% improvement in 12-month employee retention. Use Case 2: Supply Chain Optimization for E-Commerce Challenge: $2.3M annual loss from overstocked perishable goods. Solution: - Prophet time-series models forecasted regional demand - Fine-tuned LLMs analyzed social media trends for real-time demand sensing Result: 27% reduction in waste, 15% increase in fulfillment speed. Avoiding Common AI Adoption Pitfalls Mistake 1: Chasing Trends Without Alignment Example: A startup invested $500K in a metaverse AI chatbot despite having no metaverse strategy. Solution: Use a weighted decision matrix to evaluate tools against KPIs. Weight factors like ROI potential (30%), technical feasibility (25%), and strategic alignment (45%). Mistake 2: Ignoring Data Readiness Example: A bank’s customer churn model failed due to incomplete historical data. Solution: Conduct a data audit using frameworks like [O’Reilly’s Data Readiness Assessment](https://www.oreilly.com). Prioritize data labeling and governance. Mistake 3: Overlooking Change Management Example: A manufacturer’s warehouse staff rejected inventory robots. Solution: Apply the ADKAR framework (Awareness, Desire, Knowledge, Ability, Reinforcement). Trained “AI ambassadors” from frontline teams increased adoption by 63%. Conclusion The AI revolution rewards those who blend technical mastery with strategic execution. For professionals, this means evolving from coders to translators of business value. For organizations, success lies in treating AI as a core competency—not a buzzword. Three Principles for Sustained Success: 1. Learn Systematically: Dedicate 5 hours/week to AI upskilling through curated resources. 2. Experiment Fearlessly: Use sandbox environments to test tools like Anthropic’s Claude or Stability AI’s SDXL. 3. Collaborate Across Silos: Bridge the gap between technical teams (“What’s possible?”) and executives (“What’s profitable?”).
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As artificial intelligence continues to reshape industries, the landscape of AI talent recruitment has evolved significantly. Based on my recent discussions with technical recruiters and industry leaders, I want to share comprehensive insights into the current state of AI recruitment, team structures, and what both companies and candidates should know about this rapidly evolving field.
The Modern AI Team Structure Today's AI teams are increasingly complex, organized along two primary dimensions: workflow-based and layer-based structures. This complexity reflects the maturing of AI as a field and the specialization required for different aspects of AI development and deployment. Core Team Components The modern AI team typically consists of three major divisions:
A crucial addition to this structure has been the emergence of AI-focused product managers who bridge the gap between technical capabilities and business requirements. Their role in identifying viable use cases and ensuring business alignment has become increasingly critical. Technical Interview Evolution The technical interview process for AI roles has become more sophisticated, reflecting the field's complexity. While traditional coding and system design rounds remain important, machine learning-specific assessments have become crucial:
For research positions, additional components typically include:
Engineering roles, while still requiring strong ML knowledge, place greater emphasis on deployment and optimization skills. What Drives the AI Talent Movement? Understanding what motivates AI talent is crucial for successful recruitment. The primary drivers I've observed include:
Staying Connected: Industry Networks and Resources The AI community remains highly connected through various channels: Major Conferences
Digital Platforms
The Rise of AI in Recruitment Ironically, AI itself is transforming the recruitment process. New tools and approaches include:
Effective Passive Talent Engagement Successful talent engagement strategies now include:
Portfolio Assessment and Beyond One crucial insight I've gained is the importance of looking beyond traditional metrics when assessing AI talent. While GitHub portfolios provide valuable insights, some highly capable candidates may not perform well in traditional interviews. This has led to a more holistic approach to candidate assessment, including:
Looking Ahead As the AI field continues to evolve, recruitment strategies must adapt. Companies need to focus on:
Conclusion The AI recruitment landscape continues to evolve rapidly, driven by technological advancement and changing candidate preferences. Success in this space requires a deep understanding of both technical requirements and human factors. Companies must stay agile in their recruitment approaches while maintaining high standards for technical excellence. When hiring AI engineers to build Generative AI (GenAI) products during the evolution of a startup from seed-stage to PMF (Product-Market Fit) stage to Growth stage, it's important to consider strategies that align with the company's evolving needs and budget constraints. Here are some strategies to consider at each stage:
Seed Stage 1. Focus on Versatility: At this stage, hire AI engineers who are generalists and can wear multiple hats. They should have a broad understanding of AI technologies and be capable of handling various tasks, from data preprocessing to model development. 2. Leverage Freelancers and Contractors: Consider hiring freelance AI specialists or contractors for short-term projects to manage costs. This approach provides flexibility and allows you to access specialized skills without long-term commitments. 3. Upskill Existing Team Members: If you already have a technical team, consider upskilling them in AI technologies. This can be more cost-effective than hiring new talent and helps retain institutional knowledge. PMF Stage 1. Hire for Specialized Skills: As you approach product-market fit, start hiring AI engineers with specialized skills relevant to your GenAI product, such as expertise in natural language processing or computer vision. 2. Build a Strong Employer Brand: Establish a strong brand as an employer to attract top talent. Highlight your mission, values, and the impact of your GenAI product to appeal to candidates who share your vision. 3. Offer Competitive Compensation: While budget constraints are still a consideration, offering competitive salaries and benefits can help attract and retain skilled AI engineers in a competitive market. 4. Implement Knowledge-Sharing Practices: Encourage mentoring and knowledge-sharing initiatives within your team to enhance skill development and foster collaboration. Growth Stage 1. Scale the Team: As your startup grows, scale your AI team to meet increasing demands. Hire senior AI engineers and data scientists who can lead projects and mentor junior team members. 2. Invest in Continuous Learning: Provide opportunities for ongoing learning and development to keep your team updated with the latest AI advancements. This investment helps maintain a competitive edge and fosters employee satisfaction. 3. Optimize Recruitment Processes: Streamline your hiring process to efficiently identify and onboard top talent. Use AI tools to assist in candidate screening and reduce bias in hiring decisions. 4. Foster a Collaborative Culture: Create a work environment that encourages innovation, creativity, and collaboration. This helps retain talent and enhances team productivity. By adapting your hiring strategies to the specific needs and constraints of each stage, you can effectively build a strong AI team that supports the development and scaling of your GenAI products. Published by Andela Introduction
Modern tech companies realize that data teams need to consist of professionals with varied expertise, including data analysts, data engineers, data scientists, applied scientists, and machine learning engineers. Data teams work closely with cross-functional stakeholders to build data-driven products that are powered by predictive analytics as well as machine learning. Data-driven organizations rely on robust data infrastructure and ETL processes for downstream machine learning use cases. This recent development is accompanied by the rise of data engineering as a specialized discipline. As more organizations undergo digital and AI transformation journeys, the demand for data engineers has increased concomitantly. Data engineers are required to build the data infrastructure and pipelines and facilitate easy access to processed data for data scientists to build machine learning models. In this article, we’ll dive into the differences between the profiles of a data engineer and a data scientist along several dimensions, including their roles and responsibilities, educational requirements, specializations, and career growth. Roles and responsibilities of data engineers and data scientists Data engineers primarily build the pipeline system for data scientists to consume with models for various use cases. Therefore, data engineers are often hired earlier to build the data platform before onboarding data scientists. In smaller companies and startups, it is not uncommon for data professionals to do both data engineering and data science. As a company grows and scales its data science efforts, specialized data engineering and data science professionals become necessary. Data engineer’s responsibilities
Data scientist’s responsibilities
Every day, data engineers usually write code, build data pipelines, and maintain various pieces of the data infrastructure as well as serve requests for cleaned and processed data from data scientists. Data scientists typically spend most of the day developing and training machine learning models, conducting multiple experiments to optimize the model performance, and meeting cross-functional stakeholders from engineering, product, and business teams to discuss results and develop new use cases. Education differences between data engineers and data scientists Data engineers typically have a bachelor’s degree in computer science or information technology. Their core expertise is focused on software engineering skills such as programming, algorithms, data structures, systems architecture, and building software tools. With the advent of cloud computing as the foundation for any tech organization, data engineers are also expected to be familiar with relevant cloud-based technologies (like AWS, Microsoft Azure, and Google Cloud Platform) focused on data warehousing, data visualization, and data analytics. Similarly, data scientists are also able to leverage cloud-based machine learning services and APIs for common use cases such as recommender systems, computer vision, and NLP, instead of starting from scratch. Certifications provided by these cloud companies are often mandated as compulsory training during the onboarding phase for new data scientist and data engineer candidates. As data engineering is focused on building data systems for data scientists, engineers require a better understanding of statistics or machine learning to help communicate and collaborate with the rest of the data team. Data scientists have a more diverse background with undergraduate-level training in computer science, statistics, mathematics, physics, psychology, and life sciences. Data scientists often have more advanced degrees, such as a master’s degree or a PhD, in any of the above disciplines. Though data scientists traditionally had more advanced degrees, particularly the first wave which emerged a decade ago, it is becoming increasingly common for entry-level data science jobs to not have such requirements. Additionally, data scientists work with multiple stakeholders from engineering, analytics, product, and business teams, and it is helpful for them to know a bit about these areas for a smoother and more efficient collaboration. Building a successful, collaborative data product with diverse cross-functional teams requires efficient communication and storytelling skills from data scientists. Specializations With the rising popularity of data science and data engineering jobs, a number of upskilling platforms, courses, and boot camps now offer specialized, practical, hands-on training. These specializations are industry oriented and often developed by leading tech companies such as Google, Microsoft, AWS, IBM, etc. There are also many certification courses that allow candidates to learn specific data skills and signal their motivation and skill set to prospective employers. The following are a selection of specializations or certifications that a successful data engineer may have:
The following are a selection of specializations or certifications that a successful data scientist may have:
However, prospective data engineers or scientists must carefully consider which course is best suited to them given the constraints of finances, time, and interests. It is not feasible nor necessary to undertake as many courses as possible, and it is more important to focus on the courses that can truly improve your understanding and improve your candidature as a data engineer or a data scientist. Career growth differences between data engineers and data scientists Career growth prospects for both data engineers and data scientists are promising. Data engineers can evolve into related roles such as data architect or solutions architect. They can become leaders who envision and lead teams working on data platforms and also transition into more traditional engineering leadership roles. With a better understanding of core data science skills such as statistics and machine learning, data engineers can also switch to data scientist roles. The demand for data scientists has remained consistently strong for over a decade now. There are numerous entry-level positions at companies of all sizes and business domains. Initially restricted to experts with deep domain expertise and doctoral training, data science has now become more democratic with the development of tools and technologies that simplify and automate the various nuts and bolts of the data science lifecycle. Data scientists can progress further to become recognized domain experts as individual contributors or build data science teams and organizations as data science leaders. With a better grasp of software engineering fundamentals such as data structures, algorithms, and optimized coding, data scientists can also switch laterally to become data engineers or machine learning engineers. Final thoughts With rapid advances in data science and the increasing appreciation for its value in business growth, companies are actively building their data science teams and capabilities. The first step involves building the foundational infrastructure for data, a job that is carried out by data engineers. They take care of building data warehouses and pipelines and provide data that is ready to be consumed by data scientists for building various machine learning models and applications. Related Blogs
Strong engineering talent is the bedrock of modern technology companies. Software engineers, in particular, are in high demand given their expertise and skills. At the same time, there is a much greater supply of software companies and startups, all of which are jostling to hire top engineers. Given this market reality, retention of top engineering talent is imperative for a company to grow and innovate in the short as well as the long term.
Retaining employees is critical for numerous reasons. It helps a company retain experience not only in terms of employees’ domain expertise and skills, but also organizational knowledge of products, processes, people, and culture. Strong employee retention rates (>90%) ensure a long-term foundation for success and enhances team morale as well as trust in the company. A stable engineering team is in a better position to both build and ship innovative products and establish a reputation in the market that helps attract top-quality talent. The corporate incentive of maintaining high standards of employee hiring and retention is also related to the costs of employee churn. Turnover costs companies in the US $1 trillion USD a year with an annual turnover rate of more than twenty-six percent. The cost of replacing talent is often as high as two times their annual salary. This is a tremendous expense that can be averted through better company policies and culture. The onus is typically on the human resources (HR) team to develop more employee-friendly practices and promote higher engagement and work–life balance. However, in practice, most HR teams are deferential to the company leadership and that is where the buck stops. Leaders and managers have a fundamental responsibility to retain the employees on their team, as more often than not, employees do not leave the company per se, but the line manager. I will discuss best practices and strategies to improve retention, which ought to be a consistent effort across the entire employee lifecycle--from recruiting to onboarding through regular milestones during an employee’s tenure. Start at the Start More often than not, managers do not invest in onboarding preparation and processes out of laziness and indifference. Good employee retention practice starts at the very beginning, i.e., at the time of hiring. Hiring talent through a structured, transparent, fair, and meritocratic interviewing process that allows the candidate to understand their particular role and responsibilities, the company’s diversity and inclusion practices, and the larger mission of the company sets an important tone for future employees. Hiring the right people who are a good culture fit increases the likelihood of greater engagement and longer tenure at the company. Hiring managers should not hire for the sake of hiring. They should put considerable thought into each new hire and how that hire might fit in on their team. Apart from hiring, managers have other important considerations, including:
In the first few months, the new hires, the hiring team, and company are in a “dating” phase, evaluating each other and gathering evidence on whether to commit to a longer-term relationship. Most new employees make up their mind to stay or leave within the first six months. A third of new hires who quit said they had barely any onboarding or none at all. The importance of a new employee’s first impressions on the joining date, the first week, the first month, and the first quarter cannot be overemphasized. Great onboarding starts before the new hire’s join date, ensuring all necessary preparation is handled, like paperwork. Orientation programs on the join day are essential to introduce the company and expand on its mission, values, and culture beyond what the employee might have learned during the interviews. Minor things like having the team know in advance about a new team member’s join date, and readying the desk, equipment, access, and logins are tell-tale signs of how much thought and effort the hiring team has invested in onboarding. Fellow teammates also make a significant impact, whether they are welcoming and drop in to say “hi” or stop by for a quick chat to get to know the hire better, or take the new employee out for lunch with the whole team. Onboarding should not end on day one but continue in various forms. Some examples include:
A successful onboarding strategy should enable the employee to know their first project, the expectations, associated milestones, and how performance evaluation works. Keep It Up! Onboarding should be followed up with regular check-ins by the manager and HR at the one-month, three-month, and six-month mark. These meetings should be treated as an opportunity for the company to assess the new employee’s comfort level on the team and provide feedback as needed. An onboarding mentor or buddy, if not assigned already, should be provided to help the employee find their feet and learn the informal culture and practices. The manager should set up the employee for success by providing low-hanging projects that are quick to deliver and help the new hire understand the process of building and deploying a new feature using the company’s internal engineering tools and systems. With quick wins, new hires are able to build trust within the organization and gain more confidence to do excellent work. As time goes on, the role of the hiring manager becomes more prominent in coordinating regular 1-on-1 meetings, providing the new hire clear work guidelines, as well as challenging and stimulating projects. Apart from work, an introduction to the organizational setup and culture, as well as social interaction within and beyond the team is also crucial. As the new employee ramps up, it is important to give constructive feedback so that the employee can improve. Where a new employee delivers positive impact in the early days itself, the manager should highlight their work within the team and organization, and motivate the employee to continue to perform well. In addition to core engineering work, employees feel more connected when a company actively invests in their learning and development. Cross-functional training programs that involve employees across different teams foster deeper collaboration and a stronger sense of connection within the various parts of the company. Investment in employees’ upskilling and education via partnership with external learning platforms or vendors also generates a positive culture of instilling curiosity and learning. Learning new skills energizes the employees and provides them opportunities to grow and develop. They can then apply the newly learned knowledge and skills to pertinent business problems. It creates a virtuous culture that yields overall positive outcomes for the employee and employer alike, and positively influences the long-term retention rates. New employees generally feel the need to be positively engaged. A powerful mission statement can sometimes convert naysayers faster and generate a company-wide sense of being part of something impactful. This fosters deeper engagement, loyalty, and trust in the company and helps employees embrace company values, resulting in better employee retention rates. Frequent town hall meetings from the leadership enable a new hire to understand the organization as a coherent whole and their particular role in furthering the company’s mission. Listen to Feedback The diverse organizational efforts to onboard, engage, and enhance new employees’ perception of the company are bound to fail if the organization does not seek and act on any feedback shared by the new hires. Companies ought to create an internal culture of open communication whereby they seek feedback from employees via surveys, meetings, and town halls, and showcase transparent efforts in implementing employees’ suggestions and feedback. Regular 1-on-1 meetings with managers should be treated as an opportunity to gather feedback and offer the employee insights into whether and how the company is taking action on that feedback. However, in spite of organizational efforts to improve employee satisfaction and wellbeing, some attrition is inevitable. Attrition rates of more than ten percent is a cause for concern, however, especially when top-performing employees leave the company. Exit interviews are typically conducted by HR and hiring managers, but in practice these are largely farcical as the employees hardly share their honest opinions and have lost trust that the company can take care of their career interests and development. Companies can implement processes that bring greater transparency around employee decisions related to hiring, promotion, and exit. These processes will also hold HR and managers to greater accountability with respect to employee churn, and incentivize them to increase the retention rates in their teams. In past generations, job stability was a paramount aspiration for employees which meant they typically spent all their working lives at the same company. In today’s world, with a plethora of enterprises and new startups, high-performing talent is in greater demand and it is possible to accelerate one’s career growth by frequently job hopping and switching companies. Nowadays, feedback about company processes, culture, compensation, interviews, and so on, is available on a plethora of public platforms including Glassdoor and LinkedIn. Companies are now more proactive in managing their online reputation and act on feedback from the anonymous reviews on such platforms. Conclusion Employees in the post-Covid remote-working world are prone to greater degrees of stress, mental health issues, and burnout, all of which have adverse impacts on their work–life balance. In such extraordinary times, companies face the unique challenge—and opportunity—to develop and promote better employee welfare practices. At one end of the spectrum, there are companies like Amazon. In 2015, The New York Times famously portrayed the company as a “bruising workplace.” Then, in 2021, The New York Times again reported on Amazon for poor workplace practices and systems, prompting a public acknowledgment from the CEO that Amazon needs to do a better job. On the other end of the spectrum, there are companies like Atlassian or Spotify that have made proactive changes in their organizational culture and are being lauded for new practices to promote employee welfare during the pandemic. Companies that adapt to the changing times and demonstrate that they genuinely care for their employees will enjoy better retention rates, lower costs due to frequent rehiring, and long-term employee trust that conveys the company as a beacon of progressive workplace culture and employment practices. Related Blogs Data science teams are an integral part of early-stage or growth-stage start-ups as midlevel and enterprise companies. A data science team can include a wide range of roles that take care of the end-to-end machine learning lifecycle from project conceptualization to execution, delivery, and monitoring:
The manager of a data science team in an enterprise organization has multiple responsibilities, including the following:
As the data science manager, it’s critical to have a structured, efficient hiring process, especially in a highly competitive job market where the demand outstrips the supply of data science and machine learning talent. A transparent, thoughtful, and open hiring process sends a strong signal to prospective candidates about the intent and culture of both the data science team and the company, and can make your company a stronger choice when the candidates are selecting an offer. In this blog, you’ll learn about key aspects of the process of hiring a top-class data science team. You’ll dive into the process of recruitment, interviewing, and evaluating candidates to learn how to find the ones who can help your business improve its data science capabilities. Benefits of an Efficient Hiring Process Recent events have accelerated organizations’ focus on digital and AI transformation, resulting in a very tight labor market when you’re looking for data sciencedigital skills, like machinelike data science and machine learning, statistics, and programming. A structured, efficient hiring process enables teams to move faster, make better decisions, and ensure a good experience for the candidates. Even if candidates don’t get an offer, a positive experience interacting with the data science and the recruitment teams makes them more likely to share good feedback on platforms like Glassdoor, which might encourage others to interview at the company. Hiring Data Science Teams A good hiring process is a multistep process, and in this section, you’ll look at every step of the process in detail. Building a Funnel for Talent Depending on the size of the data science team, the hiring manager may have to assume the responsibility of reaching out to candidates and building a pipeline of talent. In larger organizations, managers can work with in-house recruiters or even third-party recruitment agencies to source talent. It’s important for the data science managers to clearly convey the requirements for the recruited candidates, such as the number of candidates desired and the profiles of those candidates. Candidate profiles might include things like previous experience, education or certifications, skill set or tech stack, and experience with specific use cases. Using these details, recruiters can then start their marketing, advertising, and outreach campaigns on platforms, like LinkedIn, Glassdoor, Twitter, HackerRank, and LeetCode. In several cases, recruiters may identify candidates who are a strong fit but who may not be on the job market or are not actively looking for new roles. A database of all such candidates ought to be maintained so that recruiters can proactively reach out to them at a more suitable time and reengage the candidates. Another trusted source of identifying good candidates is through employee referrals. An in-house employee referral program that incentivizes current employees to refer candidates from their network is often an effective way to attract the specific types of talent you’re looking for. The data science leader should also publicize their team’s work through channels, like conferences or workshops, company blogs, podcasts, media, and social media. By investing dedicated time and energy in building up the profile of the data science team, it’s more likely that candidates will reach out to your company seeking data science opportunities. When looking for a diverse set of talent, the search an be difficult as data science is a male dominated field. As a result, traditional recruiting paths will continue to reflect this bias. Reaching out and building relationships with groups such as Women in Data Science, can help broad the pipeline of talent you attract. Defining Roles and Responsibilities Good candidates are more likely to apply for roles that have a clear job description, including a list of potential data science use cases, a list of required skills and tech stack, and a summary of the day-to-day work, as well as insights into the interviewing process and time lines. Crafting specific, accurate job descriptions is a critical—if often overlooked—aspect of attracting candidates. The more information and clarity you provide up front, the more likely it is that candidates have sufficient information to decide if it’s a suitable role for them and if they should go ahead with the application or not. If you’re struggling with creating this, you can start with an existing job description template and then customize it in accordance with the needs of the team and company. It's also critical to not over populate a job description with every possible skill or experience you hope a candidate brings. That will narrow your potential applicant pool. Instead focus on those skills and experiences that are absolutely critical. The right candidate will be able to pick up other skills on the job. It can be useful for the job description to include links to any recent publications, blogs, or interviews by members of the data science team. These links provide additional details about the type of work your team does and also offer candidates a glimpse of other team members. Here are some job description templates for the different roles in a data science team: Interviewing process When compared to software engineering interviews, the interview process for data science roles is still very unstructured, and data science candidates are often uncertain about what the interview process involves. The professional position of data scientist has only existed for a little over a decade, and in that time, the role has evolved and transformed, resulting in even newer, more specialized roles, such as data engineer, machine learning engineer, applied scientist, research scientist, and product data scientist. Because of the diversity of roles that could be considered data science, it’s important for a data science manager to customize the interviewing process depending on the specific profile they’re seeking. Data scientists need to have expertise in multiple domains, and one or more second-round interviews can be tailored around these core skills:
Given how tight the job market is for data science talent, it’s important to not over complicate the process. The more steps in the process, the longer it will take and the higher the likelihood you will lose viable candidates to other offers. So be thoughtful in your approach and evaluate it periodically to align with the market. Types of Data Science Interviews Interviews are often a multistep process and can involve multiple steps of assessments. Screening Interviews To save time, one or more screening rounds can be conducted before inviting candidates for second-round interviews. These screening interviews can take place virtually and involve an assessment of essential skills, like programming and machine learning, along with a deep dive into the candidate’s experience, projects, career trajectory, and motivation to join the company. These screening rounds can be conducted by the data science team itself or outsourced to other companies, like HackerRank, HackerEarth, Triplebyte, or Karat. Onsite Interviews Once candidates have passed the screening interviews, the top candidates will be invited to a second interview, either virtually or in person. The data science manager has to take the lead in terms of coordinating with internal interviewers to confirm the schedule for the series of interviews that will assess the candidate’s skills, as described earlier. On the day of the second-round interviews, the hiring manager needs to help the candidate feel welcome and explain how the day will proceed. Some companies like to invite candidates to lunch with other team members, which breaks the ice by allowing the candidate to interact with potential team members in a social setting. Each interview in the series should start by having the interviewer introduce themself and provide a brief summary of the kind of work they do. Depending on the types of interviews and assessments the candidate has already been through, the rest of the interview could focus on the core skill set to be evaluated or other critical considerations. Wherever possible, interviewers should offer the candidate hints if they get stuck and otherwise try to make them feel comfortable with the process. The last five to ten minutes of each interview should be reserved for the candidate to ask questions to the interviewer. This is a critical component of second-round interviews, as the types of questions a candidate asks offer a great deal of information about how carefully they’ve considered the role. Before the candidate leaves, it’s important for the recruiter and hiring manager to touch base with the candidate again, inquire about their interview experience, and share time lines for the final decision. Technical Assessment It is common for there to be some sort of case study or technical assessment to get a better understanding of a candidate’s approach to problem solving, dealing with ambiguity and practical skills. This provides the company with good information about how the candidate may perform in the role It also is an opportunity to show the candidate what type of data and problems they may work on when working for you. Evaluating candidates After the second-round interviews and technical assessment, the hiring manager needs to coordinate a debrief session. In this meeting, every interviewer shares their views based on their experience with the candidate and offers a recommendation if the candidate should be hired or not. After obtaining the feedback from each member of the interview panel, the hiring manager also shares their opinion. If the candidate unanimously receives a strong hire or a strong no-hire signal, then the hiring manager’s decision is simple. However, there may be candidates who perform well in some interviews but not so well in others, and who elicit mixed feedback from the interview panel. In cases like this, the hiring manager has to make a judgment call on whether that particular candidate should be hired or not. In some cases, an offer may be extended if a candidate didn’t do well in one or more interviews but the panel is confident that the candidate can learn and upskill on the job, and is a good fit for the team and the company. If multiple candidates have interviewed for the same role, then a relative assessment of the different candidates should be considered, and the strongest candidate or candidates, depending on the number of roles to be filled, should be considered. While most of the interviews focus on technical data science skills, it’s also important for interviewers to use their time with the candidate to assess soft skills, like communication, clarity of thought, problem-solving ability, business sense, and leadership values. Many large companies place a very strong emphasis on behavioral interviews, and poor performance in this interview can lead to a rejection, even if the candidate did well on the technical assessments. Job Offer After the debrief session, the data science manager needs to make their final decision and share the outcome, along with a compensation budget, with the recruiter. If there’s no recruiter involved, the manager can move directly to making the candidate an offer. It’s important to move quickly when it comes to making and conveying the decision, especially if candidates are interviewing at multiple companies. Being fast and flexible in the hiring process gives companies an edge that candidates appreciate and take into consideration in their decision-making process. Once the offer and details of compensation have been sent to the candidate, it’s essential to close the offer quickly to prevent candidates from using your offer as leverage at other companies. Including a deadline for the offer can sometimes work to the company’s advantage by incentivizing candidates to make their decision faster. If negotiations stretch and the candidate seems to lose interest in the process, the hiring manager should assess whether the candidate is really motivated to be part of the team. Sometimes, it may move things along if the hiring manager steps in and has another brief call with the candidate to help remove any doubts about the type of work and projects. However, additional pressure on the candidates can often work to your disadvantage and may put off a skilled and motivated candidate in whom the company has already invested a lot of time and money. Conclusion In this article, you’ve looked at an overview of the process of hiring a data science team, including the roles and skills you might be hiring for, the interview process, and how to evaluate and make decisions about candidates. In a highly competitive data science job market, having a robust pipeline of talent, and a fast, fair, and structured hiring process can give companies a competitive edge. Related Blogs I receive several messages about the benefits of joining FAANG and similar companies and startups in the context of Data Science, Machine Learning & AI roles.
Here’s my take, in no particular order: 1. 𝐁𝐫𝐚𝐧𝐝. FAANG+ are not only the top technology companies but also the biggest companies by market cap -> great brand to add to your profile, top compensation and benefits. 2. 𝐒𝐜𝐨𝐩𝐞. The scope of AI/ML applications in these companies is tremendous as they have tons of data. You can get to work on multiple use cases, driven by statistics, machine learning, deep learning, unsupervised / semi-supervised / self-supervised, reinforcement learning etc. Internal team transfers facilitate expanding your breadth of ML experience. 3. 𝐁𝐚𝐫. The AI/ML work is cutting edge, as most of these companies invest heavily in R&D and create game-changing techniques and models. They also invest heavily in platform, cloud, services etc. that make it easier to build and deploy ML products. 4. 𝐑&𝐃. You can do both research on moon-shot projects if that’s your cup of tea, as well as more immediate business-driven data science projects with monthly or quarterly deliverables. 5. 𝐏𝐞𝐨𝐩𝐥𝐞. You get to work with the creme-de-al-creme in terms of talent, ideas, vision, and execution. Your own level will rise if you are surrounded by some of the brightest folks, and also get to collaborate with their clients and collaborators from academia, startups as well. 6. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤. After FAANG, people go on to do many diverse things — from building a startup to doing cutting-edge research to non-profits to venture capital amongst others. You can find quality partners for the next steps of your career journey. 7. 𝐒𝐲𝐬𝐭𝐞𝐦𝐬. Processes and systems for AI/ML/Data are more mature and streamlined than smaller/newer companies which can facilitate your speed and execution of your projects. 8. 𝐂𝐮𝐥𝐭𝐮𝐫𝐞. The culture, on average, is more professional as these companies invest heavily in their employees and regularly come up with new employee-friendly policies to make it a great place to work. 9. 𝐅𝐫𝐞𝐞𝐝𝐨𝐦. After FAANG, you will be in demand and recruiters and hiring managers will seek you out if you’ve proved your chops whilst at the company. You will have more opportunities to sample from and greater freedom in terms of deciding your career and life trajectory, as you can also move internally to different countries. 10. 𝐈𝐦𝐩𝐚𝐜𝐭. Given the scale at which these companies operate, the scope for real-world measurable impact is enormous. There are some downsides, caveats and exceptions as well, but on average these factors make FAANG and similar tech companies a very attractive proposition to launch, build and grow your career in data science and machine learning. Published by Neptune.ai Introduction
In 2010, DJ Patil and Thomas Davenport famously proclaimed Data Scientist (DS) to be the “Sexiest Job of the 21st century”. The progress in data science and machine learning over the last decade has been monumental. Data science has successfully empowered global businesses and organizations with predictive intelligence and data-driven decision-making to the extent that data science is no longer considered a fringe topic. Data science is now a mainstream profession and data science professionals are in high demand a cross all kinds of organizations from big tech companies to more traditional businesses. A decade earlier the focus of data science was more on algorithmic development and modeling to extract robust insights from data. However, as data science has evolved over the decade, it has become clearer that data science involves more than just modeling. The machine learning lifecycle, from raw data through to deployment, now relies on specialized experts including data engineers, data scientists, machine learning engineers along with product and business managers. The role of a machine learning engineer is gaining prominence across companies as they realized that the value of data science cannot be realized until a model is successfully deployed to production. Whilst a lot of tools and technologies such as Cloud APIs, AutoML, and a number of Python-based libraries have made the job of a data scientist easier, the MLOps of putting models into production and monitoring their performance is still quite unstructured. For a detailed look at the respective skills, responsibilities, and tech stack of various profiles, ranging from a data scientist to a data science manager, refer to my previous article on how to build effective machine learning teams in the industry [2]. There are four core steps in executing a data science project:
Thus, the definition and scope of a data scientist vs. a machine learning engineer is very contextual and depends upon how mature the data science team is. For the remainder of the article, I will expand on the roles of a data scientist and a machine learning engineer as applicable in the context of a large and established data science team. In this article, I will:
Differences between Data Scientist & Machine Learning Engineer In this section, I will discuss the primary differences in skills, responsibilities, day-to-day tasks, tech stack amongst other things. The chief responsibility of a data scientist is to develop solutions using machine learning or deep learning models for various business problems. It is not always necessary to create novel algorithms or models as these tasks are research-intensive and can take up considerable time. In most cases, it is sufficient to use existing algorithms or pre-trained models, and optimize them in the context of the problem statement. However, in more innovative and R&D-focused teams or companies, scientists may be required to produce novel research and model artifacts. On the contrary, the main goal of machine learning engineers is to take the models prepared by the data scientists and take them to production. This involves multiple aspects including model optimization to make it compatible with the custom deployment constraints and building MLOps infrastructure for experimentation, A/B testing, model management, containerization, deployment, and monitoring the model performance once deployed. These factors translate into the underlying differences in skills, responsibilities, and tech stack for the respective roles as shown in the following tables. Similarities, interference & handover Similarities between Data Scientist and ML Engineer As evident from Tables 1-3, there is a partial overlap between the skills and responsibilities of data scientists and machine learning engineers. The tech stack is also quite similar and whilst data scientists are expected to mostly code in Python, machine learning engineers also need to know C++ for porting the model artifacts into a more efficient and faster format. What machine learning engineers might lack in terms of subject matter expertise compared to data scientists, they make up for it in terms of knowledge of engineering tools and frameworks like Kubernetes that data scientists are less familiar with. Data scientists usually have a STEM background or even advanced degrees like a Ph.D. in diverse fields like biology, economics, physics, mathematics amongst others. On the other hand, machine learning engineers generally have professional experience as software engineers. While data scientists primarily deal with algorithmic and model development, machine learning engineers’ key focus is on scalable software engineering relevant to model deployment and monitoring, the remaining tasks are often common to both profiles. In a few cases, these tasks might be shared depending on the size and maturity of the data science team, and things might work smoothly. However, more often than not, especially in larger teams and organizations, this can create considerable conflict and friction especially when data scientists and machine learning engineers work in different teams and report to different managers. The handover processIt is possible to draw a clear line between the respective mandates of data scientists and machine learning engineers. Typically, data scientists will develop one or more candidate machine learning models and hand over these to the machine learning engineers following a specific contract. The contract should specify:
A structured handover contract ensures that the machine learning engineers have all necessary information to work on model optimization, any further experimentation, and deployment processes. After the handover, the data scientists become free to focus on the next machine learning use cases to take to production. The collaboration between data scientists and machine learning engineers continues post-deployment and becomes critical especially when the models break in production. As the data scientists have greater insight into the working of the model, they are better positioned to troubleshoot and fix the models. At the same time, some model failures are related to cracks in the underlying infrastructure developed by machine learning engineers, which they are in the best position to resolve. Continuous refinement of the model based on live data received by the model via active learning also falls under the domain of data scientists. Communication & Collaboration between Data Scientists & ML Engineers The success of a data science team is contingent on strong collaboration across the varied profiles [2]. Data scientists and machine learning engineers collaborate continuously during model development, deployment, and post-deployment monitoring and refinement. Ideally, if these two profiles ought to be part of the same team and report to the same leadership. In such a context, collaboration becomes easier and also fosters strong collegiality and learning from each other. However, when data scientists and machine learning engineers are part of different teams and report to different leadership, the collaboration is not as strong as it should be. In such organizational settings, data scientists and machine learning engineers do not get to interact directly as much and rely on team productivity and project management tools like Slack, Teams, JIRA, Asana, etc. For a lot of repetitive and common use cases, the use of such collaboration tools is actually a boon and saves the team a lot of time and effort. However, the transactional nature of relying on tools whose atomic units are tickets or tasks does not create a sense of team bonding and collaboration. In data science teams that rely heavily on such tools, this is a common grievance. For more complex tasks or projects, in-person or video collaboration is a must and should not be ignored by the leadership. It is often in these settings that the technical professionals might learn of new use cases or clients from the business leaders, and the business professionals in turn might learn of a new technical breakthrough that could solve up-and-coming business use cases. The same holds true for data scientists and machine learning engineers as well, where each party could learn of either a new algorithm, or a model, or a new framework to make data science more effective and productive. Current industry trends If a new version of the Harvard Business Review article in [1] were to be published in 2021, it would claim “machine learning engineer” as the sexiest job of the 2020s. While data science and model development is still a lucrative role across industry and academia, in recent years the focus in the industry has slightly shifted to building scalable and reliable infrastructure to serve data science models to millions of customers. As of today, the machine learning engineer role is in much greater demand than that of a data scientist across the tech industry.
The transition from Data Scientist to Machine Learning Engineer There are numerous online courses on learning platforms like Coursera, Udacity, Udemy, etc. but there is a relative paucity of instructors and content focused on machine learning engineering practices. While building data science models can occur in a sandbox environment like Kaggle where the models are not made to serve real-world predictions, it is only possible to learn scalable model deployment, monitoring, and related machine learning engineering tasks in a real-world industry setting. As machine learning engineering and MLOps is a more applied discipline, there are fewer experts who have the required skillset to build and maintain robust infrastructure. At the same time, existing data scientists, lured by the promise of greater potential impact, better compensation, and long-term career prospects are also seeking to transition into MLE roles. As illustrated in tables 1, 2, and 3, there is considerable overlap between the two roles. However, machine learning engineers focus on the “engineering” aspects of taking models to production while data scientists focus on developing the right set of models for specific business problems. The most relevant skills that data scientists need to learn to become an effective machine learning engineer is software engineering including the ability to write optimized code, preferably in C++, rigorous testing, and understand and build and operate existing or custom tools and platforms for reliable model deployment and management. It is definitely possible for data scientists to learn C++ and best practices in software engineering and software testing, as well as onboard new tools and technologies like Docker, Kubernetes, ONNX, and model serving platforms from multiple sources. However, since companies require machine learning engineers to have prior relevant experience, it becomes practically infeasible for data scientists to justify a machine learning profile if they do not have real-world hands-on experience in industry settings. Given the chicken-and-egg nature of this problem, the best avenue for existing data scientists to transition to machine learning engineering is with their current employer. If data scientists express interest in machine learning engineering to their managers and are allowed to shadow or even assist and collaborate with machine learning engineers on specific projects, it becomes easier to make an internal transition within the same company. This represents a challenge for fresh graduates without any prior industry experience, and a similar internal transition route from data science or software engineering to machine learning engineering is the recommended pathway. As the industry matures and companies evolve their machine learning systems and associated processes like hiring and upskilling, it will become easier for more candidates to make the transition from data science to machine learning engineering. For more complex tasks or projects, in-person or video collaboration is a must and should not be ignored by the leadership. It is often in these settings that the technical professionals might learn of new use cases or clients from the business leaders, and the business professionals in turn might learn of a new technical breakthrough that could solve up-and-coming business use cases. The same holds true for data scientists and machine learning engineers as well, where each party could learn of either a new algorithm, or a model, or a new framework to make data science more effective and productive. Conclusion AI is a cornerstone of modern enterprise. This AI-revolution has accelerated significantly over the last decade and resulted in huge unmet demand for data science professionals. Data science as a discipline has also evolved, creating distinct profiles focused on data, modeling, engineering as well as product and customer success management. Of these profiles, machine learning engineers play a critical role in taking the models developed by data scientists based on the data prepared by data engineers and for use cases identified and developed by product or business managers to fruition. Currently, the demand for machine learning engineers is similar to the demand for data scientists a decade ago. Such changes in the scope and nature of profiles in the AI industry will continue to happen, and present new challenging opportunities to engineers, scientists as well as business professionals to get their foot in the door. References [1] https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century [2] https://neptune.ai/blog/how-to-build-machine-learning-teams-that-deliver [3] https://neptune.ai/blog/building-ai-ml-projects-for-business-best-practices Published in BusinessWorld The promise of AI is real. Research from Accenture posits that AI could add $ 957 billion to the Indian economy and raise India’s income by 15 percent in 2035. Globally, the economic value that AI is expected to create close to $ 13 trillion by 2030. However, the stark reality is that India has close to 100,000 vacant data scientist jobs as of today, with the demand for AI-centric roles set to increase exponentially. How can India possibly unlock this massive economic potential of AI, without an established talent pipeline?
The lack of an established AI talent pipeline for a rapidly modernizing economy like India is alarming. While India has a working age population of close to 589 million, only 49 percent are said to possess digital skills, with the proportion of those able to understand and build AI products is far lower (World Economic Forum). Although the supply of engineering talent is steady, the nature of the rapidly changing jobs landscape means that core engineering jobs are transforming into digital roles that require strong software engineering and programming skills. Not only Indian universities have failed to keep pace with adapting the course curricula to the skills requirements of the modern data-driven industries but the consequences of not training candidates in fundamental data skills and leadership skills to build collaborative AI projects can be even more damaging to the economy in the long run. Academia suffers from an acute shortage of expert faculty to train students in state-of-the-art AI theory and practical knowledge at scale. This burden of nurturing and creating AI talent does not rest solely with educational institutions. Industry needs to step up and actively contribute by sharing business data, a critical ingredient for building data-hungry supervised AI systems, and foster a vibrant and collaborative ecosystem by partnering with both academia and startups to raise awareness of the kind of challenging business problems that only AI can solve effectively. To bridge the gap between industry requirements of AI talent and lack of industry- oriented AI education at universities, a number of edtech startups have stepped up. The majority of online edtech platforms focus on programming and coding skills, a key foundational skill to building AI systems. However, the pedagogical methods practised by most suffer from lack of imagination and creativity and do not innovate beyond offering the age-old offline classroom content via online platforms - the adage ‘old wine in a new bottle’ comes to mind. AI is a multidisciplinary field that requires strong creative, scientific and problem solving abilities to come up with novel solutions to pressing business problems. The ability to innovate beyond open-source models and solutions is fundamental to building tailored customer-centric AI solutions that incorporate the unique business and cultural context of India. If India is not able to keep pace with AI global superpowers like the USA and China, then not only is she at risk of lagging behind in the battle for tech supremacy but also faces the dire prospect of losing its emerging tech talent to countries that offer better opportunities to work at the cutting edge of AI. India is set to become the world’s youngest country with 64 percent of its population in the working age group, while western countries, China and Japan have an aging demographic. India must therefore implement policy changes, state-wide reskilling initiatives in cooperation with industry, academia and startups to reskill the nation’s youth in the latest digital and AI-first skills to steer India into the next decade as a leading digital economy. |
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