You are sitting in your office when one of the company’s data scientists comes in and tells you they’re quitting. As your (now) former analyst walks out the door, you think, “that’s third data hire who quit after just a few months.” “How can we retain more of the talent we hire?”
IT managers like yourself are well aware of the tight data science labor market. A narrow talent market increases pressure on you to retain the talent you currently have. There is a myriad of reasons why these people leave, and you can not control for every reason. However, here are some things you can do before hiring new talent that will go a long way towards retaining qualified data scientists.
Mirror Your Hiring Process to Realistic Hiring Needs
You know the challenges your data team faces, and you know how your team handles them. Make sure the job description aligns with the realities. During your interviews, be honest and upfront about what a day-in-the-life is like. From feedback, you may learn what changes need to be made to make the role attractive to new hires…and existing hires.
Perform Objective Evaluations First
As you know, data scientists need to have strong quantitative and programming skills. These skills are non-negotiable. You should test these skills first, then move onto the more subjective evaluation of soft skills. Work with your recruiting team to ensure that candidates have the needed skills before you interview them.
Use Your Current Challenges to Sell the Candidate
Most data specialists love a challenge that is intellectually stimulating. Share (without violating confidentiality) some of the projects you and your team are working on. Be realistic about how much time is spent on the more engaging projects and how selections are made for those. Including a mix of stretch projects with the day to day responsibilities could make a difference.
Collaborate and Communicate
Establish clear objectives and metrics of evaluating talent at every stage of your hiring and promotion process. By clearly managing expectations, to new hires and current team members, and incorporating objective means to evaluate success, new hires will understand how promotions and choice assignments are made, and how to work within the new system. Also, don’t forget to collaborate with the stakeholders who will be directly affected by your hire.
Many data scientists are moving into freelance work because they like the freedom and challenge of shifting from project to project employing various platforms. At the same time, these same professionals soon discover that a steady paycheck and company benefits have their advantages, too.
A data analytics talent company builds relationships with data professionals across the data science field. They know who is in the market for a temporary project role and who is ready to move into a permanent position. In addition, being industry experts, this talent partner will help you find candidates who understand the specific platforms your company employs, skills needed and how to get the right talent for the budget available.
Chisel Analytics can find the right data science and analytics talent to help you grow a strong analytics team. Chisel vets top talent with our proprietary skill assessments across 6 key elements to ensure you hire the most qualified analyst for your critical needs. We ensure that the candidate fits your mission, culture, and strategy. As a result, you get data professionals who will stick with your company and meet your short-term or long-term needs.
Chisel Analytics is now available! Sign up today to find your next data-driven challenge and start breaking down the barriers to analytics. https://t.co/DqzrcnhdoN #Analytics #productlaunch pic.twitter.com/R1zWVpAOJ1— Chisel Analytics (@chiselanalytics) April 16, 2019
The need to leverage data has created a new arms race for data science and AI professionals. Chisel Analytics can help organizations find the professionals they need to quickly scale their capabilities. #DataScience #AI https://t.co/rkrLit9Lju pic.twitter.com/kLrIhcPkEp— Chisel Analytics (@chiselanalytics) April 23, 2019
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