I recently did interviews for Data Scientist and Data Science Leadership roles. I dedicated two months to preparing, given this was the first time interviewing in 8.5 years. To prepare, I needed to refresh on Statistics and Probability, SQL, analytical, behavioral, execution interviews, etc. Below are the ways I prepared for the different types of interviews.
Resume
First, I browsed several college files to locate my resume, and then I brushed up on my resume. The latest work experience listed on my resume was my college internship. I updated my resume, adding all my professional work experience. Writing my resume was unnecessarily tricky since I needed to remember some accomplishments and project details from eight years ago when I started my career; I had to read my old performance reviews to create content. From now on, I will update my resume every six months regardless of whether I am on the job market or not, and I suggest everyone does the same. After updating my resume, I picked a few projects I could discuss in detail to share the most important and impactful projects I had worked on using the STAR method. My stories involved sharing the Situation, Task, Action I took, and the Results I drove. I incorporated metrics, analyses, and statistical concepts.
Hiring Manager Interviews
Preparations for hiring manager interviews involved reviewing my past experiences and sharing how they aligned with the roles I was interviewing for. I ensured I could talk about why I was considering working at a particular company, my interests, and the company’s products. I also had questions about the role(s) I was interested in, team structure, manager’s management philosophy and style, etc. I did my homework by reading company blogs, financials, etc. In my potential answers to hiring managers, I aimed to showcase I could operate in ambiguous areas, move quickly, influence stakeholders, show resourcefulness, highlight transparency, show relationship-building skills, and show that I was dependable.
Statistics and Probability, i.e., Quant Reasoning Interviews
I signed up for khanacademy and refreshed on Statistics and Probability concepts by going through the statistics and probability module. The module has 16 units, and it took me about two weeks to review everything. My main focus was to ensure I knew conditional Probability; specifically Bayes theorem, understood correlation, standard deviation, permutations and combinations, study design, power, effect size and sample size significance testing, confidence intervals, p values, p-value hacking, etc.
Product Interpretation/Product Sense
I focussed on understanding experimentation, i.e., how to design, set up, conduct, analyze, interpret tests, and give recommendations. On experiments, I ensured I covered everything in detail, from generating and testing hypotheses to test metrics, power analysis, creating metrics, confidence intervals, data-driven presentation, applying logical reasoning, understanding and investigating goal metrics/trends to drive product, deep dives into launch decisions and experiment results, visualizing and interpreting data, etc. I practiced solving problems while being creative, articulating, and sharing my thought process with a focus on my approach and structure. Given a situation often involving an open-ended question, I could answer why a decision would be made, how to improve the product, metrics to track and why, success measures, evaluate experiments, consider tradeoffs, i.e., short-term and long-term effects, and offer recommendations, benchmarking, and develop ecosystem metrics.
Product Leadership Interviews
I prepped to ensure I could speak to being a product leader, i.e., how to lead and develop a vision for a product. I was ready with stories from my previous roles where I collaborated with cross-functional partners to help our product team succeed in developing a strategy, roadmapping, executing, and measuring the impact of our work. I also had examples of how I influence cross-functional team members and my team leveraging data. In my previous roles, my influence was done through roadmaps, product reviews, understand work, etc. I prepped translating data into stories, making recommendations for the team, and driving strategy. In doing this, I would start broad and then narrow down on specifics. I practiced answering open-ended questions, showing confidence, and driving discussions. I also rehearsed stories to show how I handle situations where disagreements occur within my team.
People Leadership Interviews
I ensured I had stories to share from my management experience to cover situations where I cared for my team, mentored people, hired, handled performance management, helped people grow, and turned performance around for underperforming people. I practiced sharing stories where I gave feedback, which would be hard for colleagues, held a high bar for the team, and recognized people’s work. I also ensured I could speak to how I build and maintain relationships, structure the team, and set a team up for success. I had stories to share about my shortcomings, past mistakes, and what I was doing to address them. I could easily discuss my management philosophy and how I encourage mobility.
Technical Assessment, i.e., SQL Interviews
To prepare for SQL interviews, I worked on examples where, given open-ended problems, I could investigate and develop tables to answer the problems while thinking about edge cases. I practiced processing data while focusing on selecting and performing aggregate functions, joins, unions, etc. One thing I prepped was adapting my answers when given new information or constraints. Useful websites include:
- https://www.sqlcourse.com/
- https://www.programmerinterview.com/database-sql/practice-interview-question-1/
- https://sqlzoo.net/wiki/SQL_Tutorial
- https://www.w3schools.com/sql/
Cross-Functional Interviews
I prepared stories that showcased my ability to work with cross-functional teams. My stories showed how I work with various stakeholders, including taking time to understand their needs and figuring out how to work together effectively and efficiently. I focussed on showcasing my skillset on influencing cross-functional partners using stories and data and appealing to what stakeholders care about. I had stories covering how I aligned with stakeholders, delivered on my commitments, handled communication, and escalated when needed, i.e., the correct and appropriate time to do so. I also prepped for showing when I disagreed with stakeholders and how I resolved conflict.
Tools and Resources that I leveraged
- Book(s)
- ChatGPT
- I used ChatGPT to brush up on concepts by asking questions and getting answers. I would paste the job description for the roles I was interviewing for and ask chatGPT to write my resume, and cover letter, ask me questions, and answer the questions I should expect in the interview.
- Youtube Videos
- YouTube videos from Emma Ding and several data science content were valuable for understanding data science concepts and preparing for interviews.
- SQL Coding exercises/challenges
It’s important to share that I didn’t feel 100% ready for the interviews, even after studying and reviewing materials. There is an element of luck regarding the interview questions one gets, the type of interviewers, and how one handles stress in interviews. To give myself the best chance of success, I did mock interviews to get a feel of the interviews by mimicking the real experience. The mock interviews helped me come up with a structure to answer questions. I also prioritized getting a good night’s sleep before interviews to ensure I was well-rested. However, although I got in bed early on some nights, there were times when I couldn’t sleep because I was anxious and nervous about the interviews. I had this feeling for the companies I wanted to work at.
If you are preparing for Data Science interviews, I wish you all the best!!!
