Data scientists are in high demand, taking the coveted no. 1 spot on Glassdoor’s Best Jobs in America list for the past three years, and boasting a median base salary of $110,000 for those with the right skillset. 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.

If you’re about to go on 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 the role and company.

When hiring a data scientist, employers often look for business knowledge as well as mathematical and technical skills, said Jessica Hill, co-founder and data scientist at DataMinds.

SEE: Job description: Data scientist (Tech Pro Research)

“Questions from data science candidates around who in the organization will be using their work, what types of business problems the data science team helps to solve, and whether the organization is open to data scientists working with the teams implementing their insights to help drive successful outcomes are great questions to show that a candidate is interested in solving real problems rather than data science for the sake of data science,” Hill said.

Here are 10 questions that data scientists should consider asking on a future job interview.

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, vice president of data science at HackerRank. “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 will the projects I work on align to business goals?

This question will be specific to the company, and may be more appropriate for more 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.”

SEE: The Power of IoT and Big Data (Tech Pro Research)

4. Who will I be working with?

Candidates should ask questions about collaboration, said Ellen Houston, applied data science lead at Civis Analytics. “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, CIO of Power Home Remodeling.

5. 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, CTO 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.”

6. 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 type 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: Job description: Chief data officer (Tech Pro Research)

7. 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, co-founder of Gramener. “Candidates who call out the need to upskill themselves and asking 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, applied data science lead at Civis Analytics.

8. 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?” and “What are the common data quality issues you encounter? How do you deal with them?”

9. What tool set do you use, and are you open to using new ones?

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.”

10. How did the company handle a project that did not 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, co-founder and COO 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.”