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:
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