I’m a Sr. Analytics Data Scientist at a large tech firm (not FAANG) and I conduct about ~3 interviews per week. I wanted to share my transition to data science in case it helps other folks, as well as share my advice for how to nail the product analytics interviews. I also want to raise awareness that Product Analytics is a very viable and lucrative data science path. I’m not going to get into the distinction between analytics and data science/machine learning here. Just know that I don’t do any predictive modeling, and instead do primarily AB testing, causal inference, and dashboarding/reporting. I do want to make one thing clear: This advice is primarily applicable to analytics roles in tech. It is probably not applicable for ML or Applied Scientist roles, or for fields other than tech. Analytics roles can be very lucrative, and the barrier to entry is lower than that for Machine Learning roles. The bar for coding and math is relatively low (you basically only need to know SQL, undergraduate statistics, and maybe beginner/intermediate Python). For ML and Applied Scientist roles, the bar for coding and math is much higher.
Here is my path into analytics. Just FYI, I live in a HCOL city in the US.
Path to Data/Product Analytics
- 2014-2017 - Deloitte Consulting
- Role: Business Analyst, promoted to Consultant after 2 years
- Pay: Started at a base salary of $73k no bonus, ended at $89k no bonus.
- 2017-2018: Non-FAANG tech company
- Role: Strategy Manager
- Pay: Base salary of $105k, 10% annual bonus. No equity
- 2018-2020: Small start-up (~300 people)
- Role: Data Analyst. At the previous non-FAANG tech company, I worked a lot with the data analytics team. I realized that I couldn’t do my job as a “Strategy Manager” without the data team because without them, I couldn’t get any data. At this point, I realized that I wanted to move into a data role.
- Pay: Base salary of $100k. No bonus, paper money equity. Ended at $115k.
- Other: To get this role, I studied SQL on the side.
- 2020-2022: Mid-sized start-up in the logistics space (~1000 people).
- Role: Business Intelligence Analyst II. Work was done using mainly SQL and Tableau
- Pay: Started at $100k base salary, ended at $150k through a series of one promotion to Data Scientist, Analytics and two “market rate adjustments”. No bonus, paper equity.
- Also during this time, I completed a part time masters degree in Data Science. However, for “analytics data science” roles, in hindsight, the masters was unnecessary. The masters degree focused heavily on machine learning, but analytics roles in tech do very little ML.
- 2022-current: Large tech company, not FAANG
- Role: Sr. Analytics Data Scientist
- Pay (RSUs numbers are based on the time I was given the RSUs): Started at $210k base salary with annual RSUs worth $110k. Total comp of $320k. Currently at $240k base salary, plus additional RSUs totaling to $270k per year. Total comp of $510k.
- I will mention that this comp is on the high end. I interviewed a bunch in 2022 and received 6 full-time offers for Sr. analytics roles and this was the second highest offer. The lowest was $185k base salary at a startup with paper equity.
How to pass tech analytics interviews
Unfortunately, I don’t have much advice on how to get an interview. What I’ll say is to emphasize the following skills on your resume:
- SQL
- AB testing
- Using data to influence decisions
- Building dashboards/reports
And de-emphasize model building. I have worked with Sr. Analytics folks in big tech that don’t even know what a model is. The only models I build are the occasional linear regression for inference purposes.
Assuming you get the interview, here is my advice on how to pass an analytics interview in tech.
- You have to be able to pass the SQL screen. My current company, as well as other large companies such as Meta and Amazon, literally only test SQL as for as technical coding goes. This is pass/fail. You have to pass this. We get so many candidates that look great on paper and all say they are expert in SQL, but can’t pass the SQL screen. Grind SQL interview questions until you can answer easy questions in <4 minutes, medium questions in <5 minutes, and hard questions in <7 minutes. This should let you pass 95% of SQL interviews for tech analytics roles.
- You will likely be asked some case study type questions. To pass this, you’ll likely need to know AB testing and have strong product sense, and maybe causal inference for senior/principal level roles. This article by Interviewquery provides a lot of case question examples, although it doesn’t provide sample answers (I have no affiliation with Interviewquery). All of them are relevant for tech analytics role case interviews except the Modeling and Machine Learning section.
Final notes
It’s really that simple (although not easy). In the past 2.5 years, I passed 11 out of 12 SQL screens by grinding 10-20 SQL questions per day for 2 weeks. I also practiced a bunch of product sense case questions, brushed up on my AB testing, and learned common causal inference techniques. As a result, I landed 6 offers out of 8 final round interviews. Please note that my above advice is not necessarily what is needed to be successful in tech analytics. It is advice for how to pass the tech analytics interviews.
If anybody is interested in learning more about tech product analytics, or wants help on passing the tech analytics interview, just DM me. I wrote up a guide on how to pass analytics interviews because a lot of my classmates had asked me for advice. I don’t think the sub-rules allow me to link it though, so DM me and I’ll send it to you. I also have a Youtube channel where I solve mock SQL interview questions live. Thanks, I hope this is helpful.
