Are You Really Ready to Hire a Data Scientist?
How to Prepare Your Business for a Successful Data Science Hire
Hiring a data scientist isn’t just about filling a role—it’s about building a strong foundation so your investment delivers real business impact. So, what sets successful data science teams apart from those that struggle? Before you start recruiting, make sure your company is truly ready.
Why Does This Matter?
Many companies hire top data science talent with high expectations, only to find that the results fall flat, and in many cases, their hires ultimately leave the company. The problem usually isn't the people hired – it's the lack of readiness in the business. If you don’t position the role correctly or provide the proper infrastructure, even the best data scientist can’t thrive. And when they feel blocked or undervalued, they leave.
That’s why, before you hire, you need to evaluate these three key areas to see business value and be able to retain your valuable hire:
- Improper placement of the data science function within the business.
- Lack of data infrastructure to support any new hires
- Lack of development and IT infrastructure to support data science in the long term.
1. Where Should Your Data Science Function Sit Within the Organization?
To maximize the return on your data science investment, begin by strategically positioning the role within your organization. Too often, data scientists are buried in IT or isolated in departments without real business influence.
Placing the function within a very specialized area of the business significantly limits the data science function's ability to create an impact. One of the most effective ways to maximize ROI from a data science team is to position them as an internal consulting team, where they can partner cross-functionally and work directly with senior leadership to prioritize high-value analytics projects. Your data science function should report to the CEO, CTO, or another executive with maximum visibility into the company's overall goals.
Why It Impacts Retention:
Great data scientists want their work to have an impact. If their models and insights never leave the basement, they’ll seek employers who leverage their skills. Placement directly affects whether they feel empowered to solve meaningful business problems. Additionally, data scientists thrive on variety. Being able to work on a variety of business needs keeps them energized and engaged, which in turn keeps them at their best and encourages them to stay with the company for the long haul.
Data Scientists are attracted to the field of data science precisely for the value they can create within an organization. They recognize when they’re not in a position to do so and will find a place where they are.
2. Do You Have the Right Data Infrastructure?
Hiring a data scientist before your data infrastructure is in place is like hiring a pilot before you build the runway.
If your data is fragmented, outdated, or inaccessible, your new hire will spend more time finding and cleaning the data than analyzing it. Worse, they might end up doing the work of a data engineer, which not only slows progress but also leads to burnout and frustration. Any projects that the data scientist was supposed to complete can take double or triple the amount of time, which is costly to the organization and reduces trust in the abilities of the data science function due to factors completely outside their control.
Why It Impacts Retention:
Data scientists are capable of building data pipelines - in fact, they frequently do because they simply don’t have a choice. However, if a data scientist spends their first several months in a role as a data engineer and then continues to perform this role with no additional support, eventually they will leave the organization because it’s not the role they signed up to perform and takes their time away from the impactful analysis that they wanted to do.
A similar situation arises when data scientists spend more time as SQL developers than as data scientists. If all the organization’s data is stored in a nicely organized SQL database, but data scientists have to write 2000-line queries to get what they need every time they need to build a model, time is wasted, frustration grows, and you compromise the value of the role at the expense of losing top data science talent.
Before recruiting, make sure you have:
- A centralized, reliable data warehouse
- Defined data sources and ownership
- Basic data engineering or BI/SQL support and resources
- Tools that enable quick access to clean, usable data
If you're still catching up in these areas, consider hiring a data engineer or SQL developer first or working with a consulting partner to prepare your systems. Your data science team will appreciate your efforts, and your business outcomes will improve.
3. Can You Support Ongoing Development and Model Deployment?
Once your data scientists start delivering proof-of-concept models, how will those models make it into production?
Many companies fail to plan for the development and IT resources required to scale data science work. Without automation, your data scientist may get stuck running manual processes day after day, turning a high-value hire into a costly bottleneck.
Avoid this by ensuring:
- You have development support to deploy and automate models.
- There’s a straightforward process for integrating data science into products or business operations.
- Your IT and analytics teams collaborate, not compete.
Why It Impacts Retention:
Nothing disengages a data scientist faster than repetitive, manual work. When a data scientist is told that they now have to babysit a process they built rather than move on to their next exciting project, you can expect them to start brushing up their resume immediately. It is one of the most common reasons that data scientists start looking for a new role, so the impact of a miss in this area can’t be overstated. Ensuring that you have thought ahead and can reassure your talent that they have the necessary resources to automate these processes will help keep them engaged and ready to move on to their next valuable project.
When you enable seamless deployment, you keep your analytics talent in a position to accelerate ROI through data-driven decision-making.
Final Thoughts: Set the Stage Before You Hire
Before you search for your next data scientist, ask yourself:
- Have we placed the role strategically in the organization?
- Do we have the right infrastructure—data, engineering, and BI support?
- Are we equipped to scale the work and automate what has been built?
If the answer is “no” to any of the above, now is the time to make those investments. Doing so increases your chances of hiring successfully, retaining top talent, and achieving long-term business value from your data science efforts.
Need Help Hiring Data Scientists or Analytics Professionals?
At Burtch Works, we specialize in staffing and recruiting for data science, analytics, AI, and technology roles. Whether building a team from scratch or scaling an existing one, we can help you find talent that delivers.
Contact us to learn how we can support your next hire. Or explore our latest Data Science Salary Report to benchmark compensation and hiring trends.
Author: Sophia Edwards is the Chief Credit Officer at Deinde Financial, a consumer lender based in Chicago, where she leads underwriting and fraud strategy. With a background in data science and analytics and over seven years of experience building and managing data science teams, she specializes in aligning technical talent with business impact. She has over a decade of expertise in data strategy across industries, with a focus on driving outcomes through thoughtful, scalable analytics. She brings a practical, executive-level perspective to hiring and developing high-performing data teams.