Finding the Right Data Science Role: 4 Key Criteria

By on
Read more about author Lyndsey Padden.

Landing a Data Science role is one thing, but joining an organization where you can truly thrive is quite another. As a Data Science talent strategy leader, I can share my perspective on what I believe solid organizations will have in place to foster an engaged and developing data science community.

To anyone navigating today’s Data Science job market, it seems simple enough, right? Find a Data Science role at a company with a solid culture, learning opportunities, and cool work – done! For better and worse, today’s hiring climate is far more complex with opportunities spanning industries and across organizations in various places on the data maturity curve. What appears to be a great opportunity might be short-lived and have you back on the job hunt quickly. 

According to data sourced from Glassdoor, Data Science role postings grew 480% between 2017 and 2022. Today, a variety of organizations across diverse domains tout budding Data Science teams and promises of a data-driven decisioning. While we are over a decade beyond HBR’s declaration of Data Science as the sexiest job of the 21st century, the U.S. Bureau of Labor Statistics projects employment of data scientists to grow 36% between 2021 and 2031. 

The bottom line: The complexity of organizations and roles that play in the Data Science space has and will continue to grow. So, what should data scientists look for to increase their chances of joining a company that offers career path potential and not just a dead-end job? Here’s my take on where to start, along with some sample questions you can bring to an interview to get data to make an informed decision when considering offers.

1. Commitment to talent development

Hiring Data Science talent is just the beginning – the best companies will have a strategy in place for ongoing skills development. Technology skills change quickly! Companies must have perspective and practices in place to develop talent on both evolving technical skills as well as durable skills like communication, leadership, and business acumen. Look for opportunities with organizations that invest in helping you expand your capabilities through training, mentorship, and cross-functional projects. 

Interview thought starters:

What programs do you offer for ongoing learning and development?

How is learning embedded into the culture at the company?

Has your organization gone through a major technology shift? If so, how did you help reskill talent?

2. Robust community of collaborators, learners, and problem-solvers

Regardless of tech stack, domain, or applied method, data scientists are collaborators, learners, and problem solvers at heart. Companies most conducive to long-term career paths for talent tend to have established teams or have an active plan to drive connection for their talent to other Data Science professionals. If growing your own skillset is a priority, you’re most likely to thrive in an environment with other data scientists to learn from. 

Interview thought starters:

How many data scientists (or analytics professionals) do you have in the organization?

How is mentoring encouraged or supported here?

What types of roles exist within the technology organization? Are there various Data Science and analytics roles? Do you have product or engineering teams?

(If the organization is more start-up-oriented or newer to having a Data Science team) How do you keep your Data Science talent connected with industry or other Data Science professionals?

3. Stimulating, diverse work

Some Data Science jobs look great on paper but ultimately lack variety, sophistication, or technical depth. Ask about the types of initiatives you’ll be involved in to ensure the work aligns with your interests and development goals.

It is also important to understand how engrained Data Science and data-driven decisioning are an organization’s strategy. An organization with a more mature data culture will generally offer more diverse and advanced applications as well as stronger buy-in and endorsement from leadership for the value that Data Science drives.  

Interview thought starters:

Can you give an example of where Data Science drove a business decision?

What types of applied Data Science capabilities exist in your organization today? What capabilities are you looking to build out further?

4. Good vibes backed by great stats

Even before interviewing, it is wise to do some pre-work to see what others have said about the organization. Sources like Glassdoor, LinkedIn, and other social media outlets can provide a view into the tone of the company as well as how its associates engage. When interviewing, make sure you dig deeper into the company’s culture too. Look for genuine passion from recruiters and hiring managers. Trust your instincts if something feels off, and don’t forget that an interview experience is not only an opportunity for a company to assess you, but also for you to assess the company.

Interview thought starters:

What initially attracted you to the company and what makes you stay?

(Question for recruiting) What do retention and talent mobility look like? What about tenure among current workforce?

Do What Data Scientists Do Best 

Finding the right Data Science job takes effort, but following these key criteria will set you up for long-term success. Take time to thoroughly research companies and evaluate how well they align with your goals. The ideal role for leveraging your skills is out there.

My final parting advice? To all the aspiring and thriving data scientists out there looking to launch or grow rewarding careers, do what you do best: Analyze the offerings. And to all the orgs looking to attract and retain top-tier talent – foster environments that champion learning and development backed by a diverse portfolio of work that delivers real business value.