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How do we work together? 1: Introduction 👉 If the answer to the above question is yes, connect with me via an introductory call or email me at [email protected]. 2: Proposal After reviewing your career coaching requirements based on our introductory conversation or email, I will share a proposal for a personalised coaching program including a clearly defined roadmap, resources, levels of engagement, timelines, pricing etc. We discuss the proposal and agree on the various terms and conditions before we start working together. 3: Coaching We meet online regularly as agreed to discuss your queries regarding your:
Testimonials I have coached 100+ candidates and helped them crack AI/ML roles (Data Scientist, Applied Scientist, Research Scientist) at top technology companies in the USA, including: Apple, Meta, Amazon, LinkedIn, Databricks, Twitter. Check out their testimonials for my coaching services. Pricing
Career Advice I like to share my perspective on various topics related to careers in AI including upskilling, deciding what projects to work, which teams/companies to work at, best practices for excelling in your AI/ML jobs etc. You can see my blogs below.
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AI: Career advice
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Here are some tips for safeguarding data privacy in AI applications:
Here are some tips for navigating the changing landscape of AI technologies in your career:
How can you strengthen client relationships through confidence in your AI knowledge and abilities?3/9/2024 Clients want AI experts they can believe in. Here's how you can radiate confidence:
As AI continues to transform industries, it's essential to understand how it will impact your job and career prospects. Here are some strategies to help you navigate this shift and thrive in an AI-first workplace:
1. Upskill: Focus on developing skills that complement AI, such as critical thinking, creativity, and emotional intelligence. 2. Diversify: Explore new fields and industries where AI is less prevalent, ensuring your skills remain relevant. 3. Embrace AI & Automation: Leverage AI tools to streamline tasks, freeing up time for more strategic and creative work. 4. Stay Agile: Be prepared to adapt to changing job requirements and new opportunities as AI impacts your work. Creating a personalized learning plan for continuing education and upskilling in AI is crucial for staying ahead. Here's how you can do it:
1. Identify Goals: Determine what you want to achieve in AI, whether it's a specific skill or a new role. 2. Assess Current Skills: Evaluate your current knowledge and skills in AI to identify gaps. 3. Choose Resources: Select relevant courses - self-paced or live courses, tutorials, and books that align with your goals and skill gaps. 4. Set Timelines: Create a schedule for completing your learning plan, ensuring consistent progress. 5. Work with a Mentor: Identify an AI expert who can evaluate your progress, give you feedback and ensure your efforts are directed towards your goals in AI. To develop the key qualities of strong, effective leaders in AI, focus on the following:
1. Strategic Thinking & Vision: Develop a deep understanding of AI's potential and its impact on your organization. Translate it into a clear, inspiring vision for your team. 2. Communication: Clearly explain complex AI concepts and align both technical and non-technical stakeholders. 3. Collaboration: Foster open communication and collaboration with cross-functional teams to drive AI adoption and unlock the collective brilliance of your AI team. 4. Adaptability: Stay up-to-date with AI advancements & be prepared to pivot strategies as needed. Don't get lost in the tech. Understand the ethical implications and build solutions that benefit customers. The field of AI has changed dramatically over the last decade. Consequently, the role of a data scientist has also transformed and evolved into multiple specialized roles like data engineer, machine learning engineer, research scientist, applied scientist, AI product manager, and so on. I believe that we are still in the early days of AI, and it is as good a time as ever to break into data science.
Data science is also becoming more engineering-focused as companies realize that business value cannot be realized until a robust infrastructure is in place to deploy, monitor, and maintain data science models in production. As a result, data science offers an opportunity for software engineers to transition laterally and work more closely with data and models apart from code. Additionally, data science has matured as a field with the advent of several tools and products that make the entire data science life cycle more efficient, transparent, and reproducible. The organizational time, effort, and resources needed from conceptualization to production of machine learning models are reducing, enabling data scientists to drive more significant business impact. Another trend is the focus on deriving business value from massive amounts of unstructured business data like images, text, audio, and video apart from structured, tabular data. For such applications, deep learning models are particularly relevant. We are currently witnessing a tremendous amount of innovation and advances in this area, with groundbreaking models like BERT, GPT-3, DALL-E 2, Imagen, and Whisper, to name a few. We will see a more significant business impact of innovative AI R&D where startups and large companies leverage these technologies to build new products and services. It is, therefore, even more exciting to be at the forefront of AI innovation and build a long-term career in data science and AI. If you have a quantitative background in computer science, engineering, physics, finance, and related disciplines, you already have the core technical skill set to transition and excel in data science.
Candidates from a non-technical domain, on the other hand, have the advantage of domain knowledge. Doing well in data science requires a deep understanding of both the data (and the business domain) as well as the scientific aspects of analyzing data. I have seen and coached several candidates from non-traditional backgrounds in transitioning to data science and becoming successful practitioners and experts in the field. My general advice to candidates interested in data science is to realize that they might already have several skills relevant to the data science industry. You only need to bridge the gap in the skills you lack or are less confident in to crack jobs at top tech companies and startups successfully. Interviews for data scientist, machine learning engineer, and AI-focused roles comprise several rounds during a typical on-site interview. These interviews assess candidates' prowess in technical (coding, statistics, machine learning, systems design), product (product metrics, product sense, business case), as well as leadership and behavioral skills.
In a typical hour long interview, candidates may get anywhere from 5 to 15 minutes to ask questions to the interviewers. However, most candidates do not prepare or think about questions to ask in advance. This is a big missed opportunity for candidates to learn more about the role, team, org, company, tech stack, culture, leadership values etc. directly from the current employees and future team mates. In the context of data science, candidates ought to ask pertinent questions that may shed more light on the day-to-day work, projects, teams and the culture in the org. With greater interviewing and real-world data science experience, candidates will be able to better decipher the answers to such questions and read between the lines to make a more informed decision whether to join the company or not. With everything else being more or less equal amongst the different job offers one may have, the quality of the hiring manager and team, organizational culture, learning and career growth prospects become decisive factors. Following is a sample list of 20 questions to consider asking the hiring team, in no particular order:
Mathematics is an integral component of Data Science. Building a strong foundation in topics like Probability, Statistics, Linear Algebra, Differential Calculus, Optimisation etc. will hold you in good stead in your data science career.
Having said that, it is important to note that the required level of understanding of the mathematical underpinnings of machine learning methods varies depending on the type of data science role, company, and the business domain. For example, if you are a product data scientist at big e-commerce company, you may not need to dive deep into the underlying math to excel at your job. On the other hand, if you are a research/applied scientist in an R&D division or a financial trading firm, you do need to have strong fundamentals in mathematics to better understand existing algorithms and develop novel techniques. Following is a list of recommended resources to get you started in your journey towards learning the mathematics of machine learning: Resources: Machine Learning Engineering is a relatively new role in the data science job family. The ML engineer role is in high demand as organisations have realised that they cannot realise their business goals without first deploying machine learning models to production.
In large organisations with big machine learning teams, Machine learning engineer is a specialised role that is distinct from the role of a data engineer or a data scientist. In a previous article, I have compared the roles of a data scientist vs. machine learning engineer in detail. As machine learning becomes an increasingly engineering focused discipline, there is a massive requirement for professionals who combine strong software engineering skills with an understanding of the entire data science lifecycle from raw data through to model production, maintenance and monitoring. Machine learning engineers typically focus more on building pipelines and infrastructure to ensure that all the MLOps are running smoothly without any failures. I have shared a curated list of top 10 resources to help become a Machine learning engineer. Candidates can dive deeper into these courses, books, papers, and blogs to prepare for machine learning engineer job interviews.
Data science is now considered an integral function for modern companies. Data science provides companies a massive competitive edge in terms of:
Data-driven companies are able to achieve their goals faster and realise at least 20% more earnings. Proven statistics like this provide a significant impetus for business to invest in building and hiring data science teams that act as the catalyst for bringing a data culture across the entire organization. Cracking data science interviews is getting tougher and tougher. Depending on the type of company, be it a startup or a big tech, and the level that you are targeting, you can expect to have 3–6+ interviews in all. The core data science interview rounds focus on statistics, programming (Python), machine learning, product or business sense, behavioral or leadership interviews. Additionally, interviewers in each round round also assess your problem solving, thinking, and, communication skills.
DSA is not as relevant for data science interviews except if you are applying for the role of data engineer or machine learning engineer. These roles involve more software engineering than data science and therefore require strong DSA understanding. Data science interviews lack structure and vary a lot, and therefore it helps to learn from experienced mentors who have worked at the kind of companies that you are targeting. |
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