Benjamin Franklin said, “By failing to prepare, you are preparing to fail.” Data science interview preparation is often very strong when questions center on statistical and technology skills but often falls apart in the ‘sell’ of soft skills. “Soft skills woven into the story of your work experience is often what makes the difference in getting considered for the best data science jobs,” says Data Science and Analytics Executive Recruiter, Nancy Darian. Here are some tips to improve your data science interview preparation.
Which Data Science Job?
The term data science covers a number of different positions. Make sure you have determined which one is the best fit for you and prepare you answers for that skill set.
- Data Analyst – Someone who is very detail oriented and is able to pull data from SQL and/or NoSQL databases, perform exploratory data analysis and have experience with google analytics. Excel and Tableau skills are also common
- Data Engineer – Core technical skills. Design and develop data infrastructures for big data companies. Build processing systems using Hadoop, Spark, Airflow, etc.
- Machine Learning/Artificial Intelligence Engineer – Design and develop machine learning models and infrastructures, building libraries and frameworks that enable larger applications.
- Data Scientist – Cover every skill previously mentioned in the three other data science jobs and also be able to define and solve complex business, technical and algorithmic problems.
Technical Skills to Present Effectively
For data scientists openings the following four skills should be in your tool-kit and you should be prepared to discuss and give examples of using all of them.
- Programming – Python, python and python. R is fading as a primary skill needed.
- Probability and Statistics – Foundational statistics like exploratory analysis, sampling and experimental design are critical for career success.
- Data Management – Using SQL, NoSQL or Pandas to pull data out of a relational or non-relational database will always be part of the job. You may not actually do it as you move up in your career but the management of data and what is possible and what isn’t will always be part of the job.
- Machine Learning Algorithms – Models utilizing linear regression, decision trees and logistic regression are minimum requirements for the sophisticated data scientist. Knowing how they are trained and tested using data as well as experience with neural networks will put you in the top echelon.
Here is a great blog providing 50 questions and answers that you should be very comfortable with.
Very Important Soft Skills
Soft skills boost productivity, efficiency and support decision making. Weaving your soft skills into your interview narrative is essential to show your understanding and expertise in the business applications of data science.
- Communication – Link business issues with the scientific, analytical and technical facets of your skills. Convey results coherently and understandably to both technical and non-technical audiences both through effective storytelling and data visualization.
- Curiosity – Show you have a natural intellectual curiosity and a desire to answer questions people have. Be able to frame questions correctly and determine how you can help the organization with the answers leading to the proper course of action.
- Business Acumen – Know the strengths and weaknesses of your current organization, your industry and your analysis. Show evidence of your ability to tame massive amounts of data and translate it to make effective business decisions.
There are a number of other soft skills you could talk about: adaptability to the rapid technological innovation happening within the field, that you are a team player and interface well with a variety of groups within the organization and that you can operationalize into a production environment efficiently and with high quality the wonderful models you have developed.
Interested in more data sciene interview preparation advice? Contact Smith Hanley Associates’ Data Science and Analytics Executive Recruiter, Nancy Darian at email@example.com.