This blog is contributed by Katie Ferguson, one of Burtch Works’ experts in data science and analytics recruiting.
With the results from our latest data science & analytics hiring survey showing that most teams are not only hiring, but also actively looking to expand the size of their teams, competition for talent is fierce.
Last year my colleague, Brian Shepherd, wrote about how to tackle common data science hiring challenges, including how to prioritize hiring needs, emphasize the mission and goals of your team, and addressing timing issues during the hiring process.
In this post I wanted to expand on how lengthy hiring delays can hinder your ability to hire top talent, as well as strategies for avoiding common time traps during the hiring process!
5 Ways to Improve Your Time-to-Hire for Data Science & Analytics Roles
Since every stage in your hiring process can add days (or weeks) to your time-to-hire, I’ve included tips below for each layer that you can address. With candidates often interviewing with multiple companies, they go off the market quickly, so companies that make the most hires find a way to keep things moving!
1. Start the search early
Obviously, we all know that hiring can be a lengthy process, so the sooner you get started, the better. This means that the sooner you drill down on what your team’s needs are and identify your preferred candidate profile, the quicker your recruiters can start approaching potential talent. How sophisticated in Python do they need to be? How much statistical modeling will be a component in this role? Are you willing to train them on a software?
Searching for that “do it all” unicorn may be tempting, but it will severely limit your options and likely delay your search.
2. Make time for interviews after business hours
Particularly for candidates who are currently employed, making time for phone interviews during the workday can be tricky. Accommodating interviews after hours makes it easier to fit time around a candidate’s work schedule and keeps things moving. This way you aren’t trapped in a cycle of constantly cancelling and rescheduling if they get pulled into a meeting or can’t get away from their desk during the middle of their day.
If, for example, a data engineer goes on the market, they’ll likely be very busy with interviews. Being flexible will go a long way with the candidate and make your company stand out as a better place to work than competitors that won’t make time around their work schedule.
3. Carefully consider assessment length
Going to give an SQL assessment? Want to evaluate their Python coding skills? Consider whether an assessment is necessary, or whether the candidate can provide either their GitHub profile or produce other work they’ve already done. For working professionals especially, assessments longer than four hours will likely hold up the hiring process, and lengthy assessments can potentially chase away strong talent who don’t have time to complete them. We usually recommend two hours or less.
4. Eliminate the need for two on-sites
A trend I’ve noticed recently is some companies requiring two separate on-site interviews. Not only is this inefficient, but it can be very difficult to schedule both from the candidate’s perspective and for the employer! If you require additional screening after the initial on-site, it will be faster to arrange a follow-up call instead of another in-person interview.
5. Make hiring decisions swiftly
Once you’ve had your final interviews or follow-up with a candidate, don’t wait too long to gather the decision makers to reach a consensus about a potential hire! If you’re still evaluating multiple candidates, try not to leave them on the back burner for too long because the longer you wait, the more likely it becomes that they’ll be scooped up by another quantitative team.
Outstanding data scientists and analytics professionals go off the market fast, especially if they have specialty skills or industry experience that are in high demand. The more you can tighten up your hiring process to make things go swiftly and smoothly, the better a position you’ll be in to secure the talent that is the best fit for your team.
Looking to hire data science or analytics talent? Make sure to connect with me on LinkedIn!