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Hiring data scientists
Your guide to finding and hiring the right person for your organization
Top hard skills interview questions for hiring account executives
Which analytics tools and coding languages are you most proficient in?
Why this matters:
Since your next data science hire will be working with analytics tools and coding languages on a daily basis, it’s important to gauge what experience they have already to predict their ramp up time. Depending on your company’s needs, you may need someone who can dive right in. Or, you may be willing to train them in certain areas.
What to listen for:
- Working knowledge of data analytics visualization tools
- Proven ability to use coding languages like Python and Java
- Demonstrated tech savviness and aptitude for learning
What is a linear regression, and what are some alternatives — and their advantages or disadvantages?
Why this matters:
This is just one example of a specific, technical question you can use to test whether a data scientist candidate knows their stuff. It can also give you a sense of whether they’ll be able to explain data science initiatives to leaders and other staff, many of whom will have little to no understanding of data concepts.
What to listen for:
- A succinct and satisfying definition of predictive modeling concepts
- Strong communication skills and the ability to discuss technical topics clearly
- Experiential knowledge of pros and cons that goes beyond jargon
Describe an original algorithm you’ve created, including how you made it and for what purpose.
Why this matters:
This question digs into the mathematical know-how of your candidate and the specific skills and tools they have at their disposal. Skilled and experienced data scientists should have no trouble discussing how they gathered the appropriate data, what skills or tools they used to build the algorithm, and what it helped them discover.
What to listen for:
- Proven ability to apply database design, SQL, normal forms, table design, and indexing
- References to skills like analytical thinking, abstraction, and organization
- Exploration of the value their algorithm added to the business
Top behavioral interview questions for hiring data scientists
Tell me about a data project you’ve worked on where you encountered a challenging problem. How did you respond?
Why this matters:
When you’re working with data, the question isn’t if problems will arise, it’s when. You want to know that your candidate understands how to deal with data-related problems or errors. How do they correct the mistake? How do they communicate the problem to leaders, customers, or other stakeholders?
What to listen for:
- Nimbleness and ability to adapt, as well as problem-solving skills
- The ability to learn from mistakes and approach issues proactively
- Professionalism in pointing out the issue and working with others on a solution
Tell me about a time when you had to clean and organize a big data set.
Why this matters:
The best data scientists take pains to ensure that the data they’re working with is high quality. “Dirty” or disorganized data can tarnish the value of analysis and generate misleading insights, so it’s essential to know that your new hire is experienced in cleaning and organizing data, no matter how big the data set is.
What to listen for:
- Use of a variety of data cleaning tools and techniques
- Explanation of various value correction method pros and cons
- Commitment to high standards of data integrity and willingness to follow protocol
How have you used data to elevate the experience of a customer or stakeholder?
Why this matters:
Ultimately, data science is about improving decision-making and performance — whether for end users or for your company as a whole. If the candidate doesn’t understand or care about the ultimate impact of their work, they may lack the big-picture thinking that you’re looking for.
What to listen for:
- The ability to connect data to an objective business result like lower costs
- Consideration of how data science affects various shareholders and end users
- Enthusiasm and expressed desire to add more value through their work
Top soft skills interview questions for hiring data scientists
Tell me about a data professional you’ve worked with and what you admire about them.
Why this matters:
This question can help you get a sense of the traits your candidate values in the people they work with. This will give you an idea of how they’ll get along with the rest of the team and whether they’ll be motivated by their interactions with their peers. Their answer may also shine a light on the kind of data scientist they aspire to be, allowing you to gauge their level of ambition.
What to listen for:
- Interpersonal skills and eagerness to learn from mentors
- Commitment to sharing knowledge and advancing the field as a whole
- Descriptions that align with the managers or peers the candidate would be working with
Do you contribute to any open source projects?
Why this matters:
This question screens for a continuous learning mindset. But it also tells you whether a candidate is curious and collaborative. Data scientists who share new ideas, knowledge, and information with one another are better able to keep pace with the rapidly changing field, so a candidate who is active in the wider data science community is one to watch.
What to listen for:
- References to specific open source projects, such as those on GitHub
- Relevant contributions that could be useful to your company
- A growth and learning mindset, along with interest in networking
What makes you well-suited to work in data science?
Why this matters:
Answers to this question should help you gauge what a candidate brings to the role beyond the core skills and capabilities. They might talk about their communication skills that make them a great asset to team projects, or how their analytical mindset lets them approach problems from a different perspective. They may also draw on previous experiences that they can apply to the role to boost company performance.
What to listen for:
- Introspection that goes beyond the basic job description requirements
- Skills in communication, analysis, organization, and technical problem-solving
- Confidence, work ethic, and a sense of fulfillment from skills exercised in the role
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