If you’re about to attend a job interview for a data scientist position, it’s important to prepare both for questions you may be asked and for those you should ask your potential employer to demonstrate your interest in your potential role and the company.
When hiring a data scientist, employers often look for business knowledge as well as mathematical and technical skills, said Jessica Hill, chief data officer at togetherAI. Some questions can even be a great way for candidates to show interest in “… solving real problems.”
We’ll take a look at the data scientist job market as well as 13 questions data scientists should consider asking in a future job interview.
Current data scientist job market
Data scientists are in high demand, making the top five on Glassdoor’s Best Jobs in America list for nearly a decade. As nearly every company now has the ability to collect data, and the amount of data grows larger and larger, employees able to effectively organize and analyze this information for business insights are needed by many companies.
Our data scientist hiring kit places the salary for a data scientist in the U.S. between $36,500 per year to around $197,000 per year, with an average salary of about $127,000 per year. Additionally, the data shows more than 30% of salaries fall between $124,000 and $138,500.
1. How will I be evaluated?
“This shows me that the candidate is thinking about performance and what we consider important at the company,” said Sofus Macskássy, director of engineering and data at LinkedIn. “It also verifies alignment with cultural values.”
2. What would you consider a successful first three and six months?
This demonstrates that the candidate wants to know exactly how the manager evaluates success or performance, and that they have a clear idea of what success looks like.
“It’s a great litmus test for a good manager or leader,” Macskássy said.
3. How do you see this role or team changing in the short and long term?
As a prospective hire, this question helps you assess the company’s future plans and how your role might evolve over time. It shows that you’re thinking ahead and are interested in growing with the company. It also helps you avoid the possibility of being a short-term hire.
4. What does the typical career path of a data scientist on your team look like?
Figuring out progression at a company should be the desire of any data science candidate. It’s crucial to ask this question because it helps to know what growth opportunities or ceilings exist within an organization. It also shows ambition and interest in long-term career development at the company.
5. How will the projects I work on align with business goals?
This question will be specific to the company and perhaps more appropriate for senior data science candidates, Macskássy said.
“This shows me that the candidate values business impact and knows enough about the business to ask a business-related question,” Macskássy said. “Even if it is naive because the candidate does not yet fully understand the business model or domain, it does show that the candidate is thinking in the right way about prioritizing work.”
When data science candidates ask questions about the overarching goals and priorities for the organization, it indicates that they intend to align their work with these goals and help drive the organization in the right direction, rather than working in a silo, Hill said.
“The best data science solutions emerge when a clear understanding of business needs is combined with deep understanding of the data,” said Pavel Dmitriev, vice president of data science at Outreach. “A good data scientist would want to know what questions and needs business has, which they will need to work on answering.”
6. Who will I be working with?
Candidates should ask questions about collaboration, said Ellen Houston, advanced analytics leader at Qualtrics.
“I appreciate when candidates ask about collaboration,” Houston said. “We work in cross-departmental teams, which requires both passion for learning and an interest in teaching others.”
Some follow-up questions to this might be, “What is the tenure of your technical people?” and “How many contractors versus full-time employees are on the team?”
This can give you more insights into company culture, said Timothy Wenhold, chief innovation officer of Power Home Remodeling.
7. Is the data science team collaborative or autonomous?
Closely related to question 6, this question helps a candidate understand the company’s work culture, especially in the age of hybrid work. It gives the candidate a sense of the team dynamics and how they would be expected to interact with their colleagues in other departments and also within the team.
This also informs the candidate on what the day-to-day of a data scientist in that team looks like. Should the team be collaborative, an applicant can ask the next question.
8. How does the data science team collaborate with other departments?
While looking for talent, hiring managers are looking for strong communicators who will work well with other departments, said Bob Friday, chief technical officer and co-founder of Mist.
“The data scientist you want on your team is a good communicator, able to translate a problem and its solution and tell the stories that data reveals, to people of varying technical knowledge,” Friday said. “They must be able to explain the complex concepts they’re working on to colleagues who are trying to implement their findings in a way that ultimately impacts customers. If they can’t, their value is severely diminished.”
9. Where does data science fit within the organization, and who would I report to?
The data scientist role is fairly new in many organizations, so there are not yet a lot of processes in place, Wenhold said.
“When a candidate asks me these types of questions, I know that they’re really looking to understand what access they have to the stakeholders in the organization,” Wenhold said. “They want to know what kind of impact they’re going to have and how they fit into our organization structure.”
These questions can also help determine company culture, Wenhold said. Some data scientists prefer to work in a place with a startup mentality, while others want to work in the business technology department of a well-established organization. Hiring managers want to be sure they find a candidate that aligns with the team’s structure, he added.
SEE: Use TechRepublic Premium’s chief data officer job description on your next job listing.
10. What training and professional development opportunities are available?
Data science is a field evolving quickly as machine learning and other technologies develop, and many companies are scrambling to keep up the pace, said Ganes Kesari, founder and chief decision scientist at Innovation Titan.
“Candidates who call out the need to upskill themselves and ask for support upfront will definitely be seen in good light,” Kesari said.
Asking about training and professional development opportunities also demonstrates that you are a lifelong learner, said Crystal Son, executive director of enterprise data and analytics solutions at Blue Cross Blue Shield.
11. How is data collected at your company?
“A good data scientist understands that, while they can do a lot with the data, they can’t do much without the data or with poor quality data,” Dmitriev said. “A good data scientist would want to ensure they will have good quality data to work with.”
Other questions to follow this up with might include “How is the data from different data sources processed and merged,” “What are the common data quality issues you encounter,” and “How do you deal with them?”
This gets at the organization’s commitment to technology, Wenhold said.
“These questions tell me that the candidate is smart and experienced enough to recognize that they’re part of a bigger process,” he added. “I have new hires spend two weeks shadowing every department before they even open up their computers and do one analysis.
“Because while statistics are important to understand, new hires can only be effective if they understand how those stats apply to the language of our specific business.”
13. How did the company handle a project that didn’t go well, or produce the intended results?
This question helps a candidate learn if a company is comfortable with failure, and how they learn from it, said Jamie Glenn, cofounder and chief operating officer of Knock.
“Failure is an important part of data science–team members should be encouraged to fail because it means they are pushing the boundaries in the way you need them to in order to be truly creative and innovative as a team and as a company,” Glenn said. “You want to hear that, when the particular project didn’t go as planned, they took a step back and did a retrospective to see what happened, and then implemented processes or policies to improve future outcomes.”
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