Use these questions to identify a candidate’s technical knowledge and abilities
Use these questions to determine how a candidate handled situations in the past
Use these questions to assess a candidate’s personal traits and cognitive skills
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:
- Answers may include references to data visualization tools and coding languages like Python and Java.
- If the candidate isn’t proficient in the tools and languages the role requires, they should be able to demonstrate an aptitude for picking up new languages quickly.
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:
- Candidates should be able to succinctly provide a satisfying definition.
- Listen for strong communication skills and an ability to discuss technical topics clearly, without relying too heavily on jargon.
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:
- Answers may include references to skills and tools like database design, SQL, normal forms, table design, and indexing.
- A great answer will dive into the value their algorithm added to the business.
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:
- A great answer will reveal the candidate’s nimbleness and ability to adapt, as well as their problem-solving skills.
- Listen for signs that they learned something from the experience and put precautions in place to prevent the same issue occurring in the future.
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:
- Candidates may mention a variety of techniques and tools here, like value correction methods and automated cleanup tools like Paxata.
- Great answers will delve into why they chose those methods—the more specificity, the better.
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:
- Look for answers that draw a clear line from data to an objective business result, like lower costs.
- Listen for signs that the candidate always looks for ways to add more value through their work.
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:
- Strong answers may highlight the person’s impressive skills, generosity with their time, or their commitment to advancing the field as a whole.
- Pay close attention to descriptions that align with the manager or peers the candidate would be working with.
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 are known to be a collaborative bunch, sharing new ideas, knowledge, and information with one another in order 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:
- Listen for references to specific open source projects, such as those on GitHub.
- Great answers will highlight not just what the candidate has contributed, but what they’ve learned from their involvement in these projects.
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 which 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 experience that they can apply to the role to boost company performance.
What to listen for:
- Listen for answers that stray beyond the basic skills and requirements listed in the job description.
- Look for candidates who come across as confident without being arrogant.
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