3 Things to Consider Before Hiring a Data Scientist

It is commonly understood that data science can bring tremendous value to an organization. That being said, a pitfall for companies when pursuing data science initiatives is hiring data scientists without having a clear vision around their goals, business impact, and expected results.

Before embarking on the lengthy (and expensive) journey of hiring a data scientist, take a step back and make sure your organization is data-science ready. This includes developing a concrete, results-focused data science strategy and auditing your underlying data to ensure your data is accurate, consistent, and complete enough to support reliable analysis.

Step 1: Develop Your Data Science Strategy

The process of hiring a data scientist requires an immense amount of time, money, and effort. It could cost your company up to $30,000 just to find a candidate with the desired skill set and personality to fit your company. In addition to the steadily increasing salary, which currently averages around $113,000 (not including benefits), it is a huge investment. If you hire a data scientist without having a clearly defined business goal for data science, you run the risk of burning through that investment and burning out the talent.

Showing a candidate that you have a strategy will inspire their confidence in your organization and help them determine if they are up for the challenge. If you plan to hire and onboard a data scientist, you should not leave it up to them to determine their mission and where they fit. To get started on developing a strategy, have your IT team and business leaders join forces to find answers to some of the questions below:

  • What are our business problems and opportunities? Do the goals of our data science initiatives match the goals of our organization?
  • What data do we have to support analytics?
  • Which business or metric definitions vary across departments in our organization? Why do these knowledge silos exist, and how can they be overcome?
  • Can our current infrastructure support data science needs?
  • Are we prepared to change as an organization based on data science initiatives?
  • How can we effectively communicate data science results?

Step 2: Evaluate Your Company’s Data Science Readiness

Accurate and readily available data is essential for any data science project. The quality of data that you use for analysis directly impacts your outcome. In other words, if nobody trusts your results, they will not use those insights to inform their decision-making, and your entire data science strategy will flop. Set your data science team up for success by providing clean and centralized data so they can hit the ground running.

While your data does not need to be perfect, you should at least ensure that your data is centralized and does not contain duplicated records or large amounts of missing information. Centralizing key information in a data warehouse eliminates time wasted on searching for the data or finding ways to work around data silos. Creating a system that cleans, organizes, and standardizes your data guarantees reliable information for everyone. It will not only help your new data scientist produce results faster, but it will also increase trust in their results around your organization and save hours of menial data cleansing done by your IT team. While the steps to achieve data science readiness are different for every company, they should all consider the same objectives.

Step 3: Define Clear and Actionable Business Cases for Data Science

A massive part of a successful data science strategy is to understand the insights data science can provide and how your business can act on that information. Start by brainstorming a variety of use cases. Determine which ones are the most actionable, relevant and provide the best competitive edge. If any of your ideas could save money, that is another great place to start. During this process there are no wrong answers. Identifying use cases can seem intimidating at first, but there are some very easy ways to get started:

  • Ask employees which common business questions go unanswered.
  • Look into what industry leaders (and your competitors) are doing. Whether it’s personalizing marketing messaging to customers or using models to identify insurance fraud, data science has use cases in any industry.
  • Find out what executives wish they could predict about your organization.
  • Reach out to experts. Many organizations (consulting companies and vendors) have implemented data science solutions at a variety of clients.
  • Identify time-consuming and complicated manual processes. Data scientists can likely automate these and make them more reliable.

Ryan Lewis is a Managing Consultant at 2nd Watch.