Interview Tips for O.R., Analytics and Data Science Jobs

Editor’s note. This article is the second in the series titled “Analytics Advice” in which topics and content draw from the collective experience and wisdom of the INFORMS community to provide advice on topics of interest to analytics students and early-career professionals. A question is posed to the community and the responses are edited into an article. If you have a topic for which you would like analytics advice, please email [email protected] with the suggestion.

“Analytics Advice” is sponsored by the INFORMS Analytics Society.

Interviewing for jobs in analytics can be complex. Conveying just your technical aptitude is usually insufficient; you also need to demonstrate your business understanding and soft skills. To help navigate the interview process, the INFORMS community was asked for their advice regarding “tips when interviewing for an O.R./analytics/data science job.” The following is an edited version of the best advice received through INFORMS Connect, along with some additional commentary. (The advice is not directly attributed to the respondents; however, the full list of respondents is provided at the end.)

1. Do your homework!

Although it may seem obvious, the top response by far was to research the company before the interview. Don’t arrive for the interview thinking you only need to discuss your resume. Read financial reports to understand the main product lines, primary revenue sources, recent accomplishments and future goals. Scan news articles to understand any positive or negative press regarding the company. If you are fortunate enough to know someone who works there, ask what they like and dislike about the company.

Be familiar with the job description, have specific questions ready and come prepared with comments on relevant functional areas (e.g., marketing, supply chain) both within the company and more broadly in the industry.

The research should not stop at the company level. The interview will most likely be a series of interviews. Ask in advance for a list of interviewers and their titles. Have a sense of how you would interact with them if hired. During the interview, ask questions about their role in the company and how long they have been there.

As one respondent said, “When I worked at a big retailer, it was always a major plus if the interviewee took the time to learn about the company on their own. It showed initiative and suggested they would actually be interested in working with the business side.”

2. Place your technical skills in a business context.

Instead of just listing skills such as Python, SQL, SAS, R, etc., provide examples of real-world projects in which you have effectively applied these skills. Discuss how your analytics skills can solve business problems, drawing on any industry-specific experiences you may have. The key is not just to say you worked on a project but also to describe what you contributed to the project.

If you are interviewing right out of school with no real work experience, focus on practical projects that you did during your program. If you have no practical project experience, you can mention certifications or classes that demonstrate the skill, especially skills mentioned in the job description.

If you solved hard problems, hint at the cleverness of your work but then explain why it is important. Be able to discuss hard problems with a 30-second “elevator pitch” that is understandable to someone who is not an expert on the topic. 

3. Provide an honest self-assessment.

Be upfront about what you have done, what you can do and what you know. If you’re strong in machine learning but weak in databases, say so. Do not attempt to inflate your skills. Don’t be afraid to say, “I don't know” or “I’m not familiar with that.” It is unrealistic for a company to expect you to know everything.

If appropriate, share an example of something similar that you learned quickly. Be specific and avoid generic statements such as “I’m a fast learner” or “I’m a hard worker.” If the company is using software or a methodology unfamiliar to you, ask questions to understand why they find that tool valuable to their work.

4. Be prepared for an assessment of your technical skills.

It is likely that one or more interviewers will probe the depths of your technical ability, whether it be programming skills, algorithm design or theoretical concepts. You should be able to explain any topic you have listed on your resume.

If presented with a topic you don’t know well, the interviewer might be trying to understand your critical thinking skills and how you approach problems. Again, be honest and don’t try to bluff. Instead, you should describe how you would address this issue. Demonstrate that you understand how to properly frame an analytics problem and design a logical solution approach.

Don’t be afraid to take time during an interview to think through your answers. If necessary, ask for a moment to consider the question before answering.

5. Demonstrate your soft skills.

Soft skills are more subjective than technical skills but are every bit as important. Sometimes soft skills are reflected in what you say, but often they are reflected in how you say it and the impression you leave. These include:

• Communications – are you able to articulate your background and capabilities in a clear and concise manner, without going into overly long explanations or losing the interviewer in technical details?
• Listening – are you paying attention to the interviewer, making eye contact and appearing genuinely interested in what they are saying?
• Teamwork – can you describe how you have worked on a team and your role? This might involve a project in which a compromise was necessary or conflict was averted. You want to leave the impression that you would be a valued member of the interviewer’s team.
• Adaptability – do you appear interested in contributing where the company has the most need, or do you appear to only want to work on what is of interest to you?
• Positivity – do you have a good attitude and seem like someone motivated and eager to contribute to the company’s success?

By carefully preparing for the interview, you increase your chances of standing out in a crowded field of candidates. This preparation indicates not only your technical competency but also your initiative and desire to contribute meaningfully to the company you hope to join.

We welcome all students and early-career professionals to join the Analytics Society to foster relationships, create networks and gain more analytics advice.

Source. This article was compiled by Dave Hunt. The respondents include (in alphabetical order) Ralph Asher, Aaron Hussey, Marc Meketon, Shannon (Xiaonan) Shang and Erick Wikum. Thanks to all the respondents!