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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.
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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:
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.
Conducting innovative research in AI is not straightforward. Researchers should focus on a problem area that they are deeply passionate about, e.g., NLP, multi-modal AI, computer vision, speech, synthetic data, graph-based models etc. For research, the problem could be in the theoretical realm. However, if the problem area is grounded in the real-world, then practitioners can actually test their algorithms on real-world data and learn from the feedback.
The most important skill for doing novel research is to think deeply about a particular problem, and apply the scientific method systematically. This involves coming up with relevant hypotheses and conducting several experiments using the right datasets, algorithms, models etc. to test the validity of the hypotheses. An empiricial, data-driven strategy coupled with creative ideas usually leads to novel research output. To come up with innovative ideas, you need to know the existing literature and what ideas have previously worked or not worked for a particular problem. Sometimes, it is sufficient to translate existing ideas for your particular use cases as well. Knowing what ideas can generalize and are practically feasible to solve a business problem is a rare skill that distinguishes the best applied researchers from the rest. To become an expert in any discipline, it is important to build a solid knowledge base which can take a significant amount of time. If you build this foundation earlier than others, then you can advance on the journey faster and develop better first-principles thinking and intuition for a variety of machine learning problems. This is particularly true in the case of AI, which requires strong fundamentals in diverse topics including statistics, mathematics, programming, data analysis, presentation, and communication skills.
However, regardless of how early or late you start your career in data science, the key is to keep practicing and honing your skills, given that the field of data science is going to continue evolving rapidly. I have worked with both Bachelors’ students as well as senior IT professionals in their 30s and 40s who are equally motivated to launch their careers in data science. Given the lack of formal degree education in data science, every data scientist I know is self-taught to an extent. With so many open-source resources, courses, code repositories and datasets available online, any ambitious and motivated person can become a good data scientist. However, to truly become a versatile data scientist, one needs to complement their learning with training from experienced industry mentors and develop a deep understanding of business domains like e-commerce, healthcare, fintech and how data science practically works in industry. |
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