#DATA SCIENTIST SAS INTERVIEW QUESTIONS HOW TO#

#DATA SCIENTIST SAS INTERVIEW QUESTIONS HOW TO#

Question to ask: how to you share code amongst the team? Is all code shared or just some of it? Not having methods of sharing code means the team canвЂ™t work together. Occasionally teams donвЂ™t share code at all and work just lives on the data scientists individual laptops. Other teams instead use methods like shared network folders, which donвЂ™t let you see when things changed, why they are changed, or previous versions.

No version control: Mature data science teams use git to keep track of changes to analyses and code.

Question to ask: how does the company balance spending time on complex approaches like AI with foundational work like cleaning data, checking data quality, and adding logging? You might get blamed when their unrealistic expectations meet reality. Machine learning and AI require a company to have a high level of data science maturity, including understanding how to build models, their limitations, and how to deploy them.

No understanding of the data hierarchy of needs: Similar to not having a data infrastructure, some companies get really excited about concepts like AI without having the foundation in place.

Question to ask: what languages does your team use, and how do you decide whether to adopt a new one? ItвЂ™s fine to use R, Python, or even dare we say SAS, but just have a consistent set of languages amongst the team. Every data science task will have a single person responsible for it, and if they quit, get sick, or just need help no one will be able to do so, creating a very stressful environment. There is a huge problem with this: when everyone uses separate languages, no one will be able to pass off work to anyone else. The idea is that if everyone uses their favorite languages work will be completed faster.

No standard set of languages on the team: Many data science teams take the approach of letting anyone on the team use any language they want.

Question to ask: what steps does the team take for QA and peer review? If the team doesnвЂ™t consistently do these, mistakes wonвЂ™t be found until the work is already delivered, which usually ends with someone getting reprimanded. These can include code reviews, practice presentations, and consistent check-ins with the team. A strong data science team will have ways to ensure mistakes donвЂ™t slip through the cracks.

No peer review between data scientists.

Question to ask during the interview: what is your data infrastructure like and who maintains it? What format is the data typically in (Excel, a SQL database, csv)? If you feel qualified to take on the role of a data engineer that may be okay, but otherwise youвЂ™ll be struggling to deliver anything of value. A data engineer is a person who prepares data for analysis, and if your company doesnвЂ™t have them youвЂ™ll have to do the work yourself. If the company doesnвЂ™t have a well-maintained data infrastructure, you wonвЂ™t have what you need to do your job. Data science requires data to be easily available for analysis. No data engineering or infrastructure.Red flags on how the data science team runs If thereвЂ™s just a data science leader and theyвЂ™re building out a team, ask how they plan to handle the issues raised below, but keep in mind itвЂ™s always easier to promise an ideal system than implement one. Someone has to do it, but we generally advise against it for your first data science role unless you come from an engineering background and want to do that work. If youвЂ™re thinking of joining a company as their first data scientist, youвЂ™ll face a whole different set of challenges, including most likely doing a lot of data engineering work (see flag 1) and spreading a data science mindset.

The first six mainly apply to companies that already have multiple data scientists or analysts. Here is our list of 12 signs the company you are interviewing with for a data scientist job should be avoided (and the questions to ask during the interview). Companies will never straight up tell you they are bad to work for, so you have to look for the signs yourself. While you can research the company beforehand on glassdoor and similar sites, interviews are the best place to get a deeper understanding of the company and ask important questions. When interviewing for any position, you should be evaluating the company just as much as they are evaluating you. Check out the rest of her blog posts, including ones on prioritizing data science work, hiring data scientists, and what to do when your data science project isnвЂ™t working. This post was co-written with Jacqueline Nolis, Principal at Nolis, LLC.

Data scientist sas interview questions