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Mike Alaimo

Partner & Talent Services Leader

The Top 6 Mistakes for Hiring Analytics Talent

Traditional approaches no longer work in today’s hyper-competitive data and analytics hiring climate. Avoid making these common analytics recruiting missteps – otherwise, you risk losing time, money and your team’s morale.

  1. The wrong person attempts to lead the search
    When it comes to filling key analytics or data scientist roles, working with a generalist recruiter is the most common error we see. Internal talent acquisition specialists spend their time juggling numerous open jobs and mounds of resumes, all the while coordinating interviews, offers and negotiations. General staffing agencies’ knowledge of analytics is limited to a few buzz words. Even specialized analytics recruiters don’t go deep enough – because without hands-on analytics experience, they can’t ask the right questions to comprehensively plan for the role or fully vet candidates (see #4).

    So, where to turn? Seek out a recruiting partner with vast experience in your specific role. We’ve seen data and analytics roles remain unfilled (or worse: filled with the wrong hire), because generalist recruiters and even tech specialists can’t nail the right fit.

  2. Rushing the planning process
    Don’t signal that your company isn’t in tune with the technologies, responsibilities and expectations for your position by using an outdated or inaccurate job description. As tempting as it is to get started quickly, if your job description looks like a standard shrink-wrapped product, you won’t differentiate and neither will your candidates.  A job description that’s misaligned with the market and lacks sufficient detail on qualifications will attract the wrong candidate, especially if you haven’t taken the time to distinguish between average and top-tier characteristics. If you don’t have set criteria for rating your candidates, your team won’t be in sync, leading to subjective feedback and poor hires.

  3. Hoping that top talent will find you
    A “post and pray” mentality is wishful thinking. Don’t wait for candidates to respond to your posted job – top analytics pros have too many other employment opportunities vying for their attention. If you’re not playing offense and networking in person and online, you’re missing prime opportunities to engage. Seek out events that appeal to data and analytics professionals – you’ll meet top notch people who aren’t actively looking, but may be receptive to your role or know someone else who might be.

    Corporate talent acquisition specialists and general tech recruiters generally don’t know where to find passive analytics talent; instead, they source candidates reactively. If your sourcing plan relies on incoming applicant flow and job board searches, you’ll miss some of the best data and analytics professionals out there.

  4. Not properly vetting competencies and skills
    Accepting a candidate’s answers at face value is another major mistake that happens too frequently. Why? Because general recruiters, staffing agencies, and even specialized analytics recruiters aren’t technical enough or lack the training to dive deeply into analytics topics in context. They may not know what questions to ask or how to ask them (i.e., using open-ended scenarios) to reveal a candidate’s technical proficiency or decision-making process. First-level responses are precisely when to peel back the layers of the onion, rather than accepting superficial answers.

    If you use take-home assessments as another level of vetting, here’s another common mistake: assessments that are too long, misaligned with a role, difficult to interpret, or over or under weighted in value. Take-home assessments are a best practice, but without a trained team and an objective evaluation process, you risk making the wrong hire.

  5. Over or underpaying for analytics talent
    Companies often think they need a data scientist when they actually need a data analyst. That mistake can cost you thousands of dollars per hire – and send a newly-hired data scientist for the door once they learn AI/ML projects aren’t core to their role. Conversely, paying lower salaries than the market commands – say, in the bottom quartile – means you’ll attract and land bottom-tier candidates only.

  6. Taking too long to make a decision
    Being indecisive is a sure-fire way to fumble your hire. This happens when too many team members are involved in the interviewing process, or when too many separate steps are added to your process. In truth, you don’t have months to conduct an interview process – in some cases, you’ve got weeks or just days to make an offer on a top candidate, otherwise you’ll lose your top hire to another company who acted faster.

    The data analytics talent market is white hot. Seasoned analytics professionals will quickly pick up on whether you understand the analytics talent market, their competencies and skills, and your clarity and vision for the role. Be informed and strategic at the start of your hiring process. That way, you’ll be able to act with speed and accuracy at the end.

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