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In 2012, Harvard Business Review announced that Data Science will be the sexiest job of the 21st Century. Since then, the hype around data science has only grown. Recent reports have shown that demand for data scientists far exceeds the supply.
However, Entry level data science can be really competitive because of the supply/demand dynamics. Data Scientists can come from all kinds of fields, ranging from Physics, Maths, Statistical background to Computer Science. Some may see this as an opportunity to rebrand themselves to influence freshmen looking to land their first role.
This list is created based on the difficulty level based on how time-consuming the task is ( Easier to tick done is listed as high priority and tasks which may take some time like learning some methodology in python is listed as a low priority)
Research about the position you’ve applied
I would highly recommend looking over the description of the position and trying your best to find out what you would be doing. The type of position will heavily influence what kind of questions you would be getting in your interview.
Will you be…
- Designing and interpreting experiments to test variants of the product?
- Expect some questions regarding A/B testing, questions regarding which metrics would be best to optimize, and questions about how to best evaluate your experimental results.
- Doing deep dives to understand more about how users use your product?
- Expect questions that test your ability to carry a data project from end-to-end, and to effectively and faithfully communicate your findings. Expect to discuss projects from previous experiences or your education and communicate what you were able to find and what you did.
- Doing applied research on inference, prediction, or optimization problems?
- These positions are a lot more custom and may require a PhD. I recommend reading through the job description to see what they might be looking for, and studying up on academic techniques to solve some problems that the team you’re interviewing for may be facing.
- Developing algorithms for a data product?
- For example Uber’s Surge Pricing feature or LinkedIn’s People You May Know feature. Depending on your specific role, you may be getting a traditional software engineering interview with a focus on processing large amounts of data, or be asked about your previous experience with solving large-scale, difficult, and custom data problems.
Of course, there are many more roles of a data scientist — so do your research on both the product and the role before you set foot in the interview room.
Ultimately the key question you should be asking yourself is — within my role at the company, what is the best way to best understand and improve the product and the business using data?
Tinker around with the product
If you can use the product, use it as much as possible before the interview. One type of data scientist is heavily involved in the process of making decisions to help improve the product and the features — and to understand the product quantitatively as much as possible.
Let your curiosity run free, and answer questions like —
how can this product be improved?
What kind of metrics would you define to measure its success?
How could this product monetize?
How could this product make more money?
How would you define engagement on this product?
What could be some friction points?
What are the key funnels or actions that you want your users to go through?
Show that you understand the system that as a data scientist you’re going to be working to improve.
Familiarize yourself with the product, as it’s very easy to reveal your lack of preparation if you don’t have basic knowledge of the product you may be working on. Also, interviewers will likely ask you data-related questions about the projects they are working on.
Think of ways on how to improve the product
After playing around with the product as much as you can — ask yourself the following questions:
- What are the aspects of the product that you really enjoy? What are your favorite features? Why do you think those features exist?
- What are aspects of the product that you don’t enjoy? Why don’t you enjoy them? Why might the product even have such a feature if there are people that don’t enjoy it?
- If you could suggest some new features for the product, what would you recommend? Is this something that is aimed at increasing growth, engagement, revenue, or brand value? Do you think that your recommended features would be high-ROI?
- What are the ways in which the company could use data to help improve the product, that it doesn’t seem to be doing already?
These questions will get you in the shoes of thinking about the product and various tradeoffs that are done in making product decisions. This gets you in the right mindset to answer some of the questions that you might get about the product and what the data scientists are working on in helping make it the best it can be.
Define some key performance indicators (KPI) for the product
After playing around with the product, think about this: what are some of the key metrics that the product might want to optimize? Part of a data scientist’s role in certain companies involves working closely with the product teams to help define, measure, and report on these metrics. This is an exercise you can go through by yourself at home, and can really help during your interview process.
There’s a useful intro to many commonly-used metrics at 16 Ways to Measure Network Effects
Review your statistics and experimental design
If you’re interviewing with a consumer internet company, chances are that they do some sort of A/B testing to decide on feature launches. This is usually one thing that many candidates are unprepared for when they start looking for data science positions, mostly because many universities don’t offer too many statistics classes. Understanding experimental design, what A/B testing is, and how to interpret results statistically are extremely important if you’re interviewing with a company that does A/B testing.
Ronny Kohavi (head of experimentation at Microsoft) has useful answers on applied A/B testing.
Review your coding and SQL
Data science positions that include some basic software engineering in the role will feature a scaled-down version of a typical software engineering interview. Normal prep for software engineering interviews will help here, as often you’ll be expected to implement code that accomplishes a certain task on the whiteboard.
Data science positions that feature a heavy “analytics” component in the role may evaluate you on SQL. I think SQL is one of the most straightforward topics to prepare for, given its more limited scope and availability of preparation resources. I list some of my favorite resources at William Chen’s answer to What is the best way to learn SQL for data science?.
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Okay, without further ado: Let's Jump up direct to the question again. What are the best books about data science? For…www.jayasekara.blog
Thank You!
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