Companies often tell me that they’re having trouble hiring data scientists. Maybe they can borrow some ideas from their sales team?
Hiring data scientists, data engineers, and related roles continues to prove a challenge for companies of all sizes and domains. Even if you’ve developed a data strategy to guide you, your efforts may still turn up lots of false leads or even no leads at all.
That’s a sign to change how you’re hiring. If you’re looking for ideas on how to source, screen, and on-board new hires, do yourself a favor and borrow some ideas from your sales team.
Why the sales team? Because, when done well, hiring and sales have a lot in common: you source and qualify leads, you extend some of them an offer, and then you provide additional support to on-board them. Your sales team does this and more on a daily basis. Show them your hiring process and they’ll likely provide a lot of useful feedback.
Don’t have a sales team in-house? No worries. Here are some ideas to get you started:
You might think that you have a process, but what I usually see – “write a job description, interview people, extend an offer” – is pretty loose and low-value. You need a consistent set of steps that will determine where to look for leads, how to contact them, and how to work with them through the rest of the sales funnel.
That last point is key: having a sales funnel means that you have a lot of people come in at the start of the process (the “top” of the funnel; here, sourcing candidates) and very few people at the end (the “bottom”; on-boarding of a new hire). Along the way, the funnel narrows as people fall out at different stages: a prospect doesn’t demonstrate the right skills, or doesn’t interact well with the team, or isn’t interested in joining the company, or whatever else.
Consistency is similarly important. Like a scientific experiment, you don’t want actions or order to vary based on who is performing the work, or what someone feels like doing that day. You want clear definitions of each stage of the funnel and any terminology used therein. (For a great explanation of this, check out Michael Brenner’s “An Inquiry is Not A Lead.”) It may seem nitpicky to be so precise, but agreeing on terminology is the first step to visibility, which is a fancy term for everyone on the team knowing what’s really going on.
A sales team doesn’t just define a process and let it sit. They track what works and what doesn’t, using formal metrics and informal observation.
Proper metrics will provide insight into how long people sit in each stage of the funnel and when they’ll most likely exit. (Advanced metrics will spot a desirable lead and engage them before they exit prematurely, not unlike how a a subscription company combats churn.)
Metrics also bring cold reality to light: they’ll show you that a certain source of leads is reliably lousy, or that your intuition doesn’t match what actually happens. You may find that many desirable prospects fall out at a certain stage of the funnel. “Where do we stand this month as far as hiring?” is a tougher question to dodge when you have hard numbers.
The numbers aren’t there to place blame, either. They’re required for the most important part of having a sales process: providing a feedback loop through which to modify and improve it over time. If you see that people get held up a lot in one stage of the process, or if desirable candidates are leaving right before you extend an offer, you can tweak that step in an attempt to improve the outcome.
You’ll sometimes cast a wide net and convince people to come to you. In other cases, you’d like to approach a specific person. You’ve reviewed their online presence, attended their conference talks, and gotten their contact info from their website. What next?
My data science colleagues have complained that, in many cases, you waste the opportunity: someone from your company reaches out, poses a number of questions from a script, and sits in dumbfounded silence when asked any detailed questions about the position they seek to fill.
Compare that to a sales rep who is pursuing a particular company (say, for an enterprise account): they study up on the products they hope to sell, the industry in question, and the prospects with whom they expect to meet. They enter the conversation with useful talking points of how the services they sell will help the prospect improve and otherwise meet their goals. This advances any sales conversations because they can engage in intelligent dialogue, pose meaningful questions, and provide relevant answers.
In short, good sales reps do their homework so they can make the most of every point of contact as they guide prospects down the funnel. Do the same with your hiring efforts and you’ll increase your chances of success.
Have you ever noticed that sales reps tend to be very positive and upbeat? It’s an infectious optimism, one based on the assumption that the deal will close. The process becomes a partnership in which the sales rep and the prospect work together to reach that goal.
If this sounds like your hiring process, yours is a rare case. Many companies are so clumsy – perhaps even arrogant – with their hiring, that the process quickly devolves from “we’re interested in talking” to “we assume you’re not a fit, and here’s a series of challenges so you can prove us wrong.” This leads to a contentious interaction and, for those who are accepted, a hazing ritual that they expect all future prospects to endure.
The most important lesson you can borrow from your sales team is to actually sell the damned job. Don’t forget that your goal is to extend an offer to someone. The hiring process isn’t for you to show off how smart you are, or how tough your company is, or anything about you at all. It’s about showing someone that yours is a great place to work and that they would be an amazing fit.
If you’re on the hiring side of the table, the data science supply/demand mismatch doesn’t show any sign of improving. Treat your hiring process as a sales process to stack the deck in your favor.
Are you having trouble hiring people for your data science team? Talk to me, I’d love to help. I can assist with writing job postings and interviews, and for companies newer to the data game, I can help you overcome the “cold start” problem by being a voice of data science before you’ve made that critical first hire.