Springboard.com reports that data science jobs are estimated to grow by 30% in this decade. Have you done your data science interview preparation?
Standard Interview Preparation
No matter the type of position you are interviewing for or where you are in your career, always, always do these four things.
- Research the role and the company. Review their website and LinkedIn page. Reach out to people in the company who work in the area where you are interviewing . If you know the people you will be interviewing with, review their backgrounds. Google them!
- Update your resume and any other portfolio you will present online or on paper. In this era of eight seconds of attention, is there anything you can make briefer but still have impact? Tailor both to the company and the job you are applying for. Move the most relevant projects to the top of the list.
- Know your salary expectations. Be prepared to present a range of base salary you would consider, and do some soul-searching before the interview to come up with the “right” numbers. Stick with what you must have to make this change or to be paid what you view as your value.
- Prepare questions for your interviewers. Always, always show your interest and insight by asking relevant, insightful questions about the company and the position. Lack of inquisitiveness is a negative in any role at any company, and having no questions is a red flag.
Data Science Interview Preparation
Brush up on foundational statistical concepts. These include probability, hypothesis testing, descriptive and Bayesian statistics and dimensionality reduction. Make sure you are prepared for verbal and written testing on statistical analysis like variability, probability distributions, logistic regression, linear regression and statistical significance.
Top 81 data science interview questions
Be prepared to show you are comfortable working with data. Demonstrate you know how to source data, clean it and can use the right tools in analyzing it. Have examples of your experience in working with very large data sets and the issues you encountered and how you resolved them.
Provide clarity on your level of expertise with Python, R and SQL. The minute you say you are an expert in all three, it will be understood you are not. Experts are the first to say they have more to learn. Be prepared to talk about how you have dealt with the most common coding questions and have examples to back that up in your portfolio.
Quick review of the most common Python coding questions, and top Python interview questions and answers
Show the experience you have in data visualization and presenting your findings. Give examples of presentations you have made to senior management where you explained technical concepts to non-technical stakeholders. This might be an opportunity to “teach” your interviewer what a data scientist can do and what might be an unrealistic expectation on their part. Educating your prospective new employer on the breadth and depth of what a data scientist can and can’t do will pay off in your success in a new job. Of course, it is a delicate balance but one that is cropping up more and more as an issue in data science job searches. The client wants what a data engineer, data analyst and data scientist would normally provide in the process in one person. Very, very tough positions to succeed in.
