A Better Talent acquisition Framework
Although many articles have been written lamenting the current talent shortage in analytics and data science, I still find that the majority of companies could improve their success by simply revamping their current talent acquisition processes.
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We’re all well aware that strong quantitative professionals are few and far between, so it’s in a company’s best interest to be doing everything in their power to land qualified candidates as soon as they find them. It’s a candidate’s market, with strong candidates going on and off the market lightning fast, yet many organizational processes are still slow and outdated. These sluggish procedures are not equipped to handle many candidates who are fielding multiple offers from other companies who are just as hungry (if not more so) for quantitative talent.
Here are the key areas I would change to make hiring processes more competitive:
- Fix your salary bands – It (almost) goes without saying that if your salary offerings are outdated or aren’t competitive to the field, it will be difficult for you to get the attention of qualified candidates; stay topical with relevant compensation grids.
- Consider one-time bonuses – Want to make your offer compelling but can’t change the salary? Sign-on bonuses and relocation packages are also frequently used, especially near the end of the year, when a candidate is potentially walking away from an earned bonus; a sign-on bonus can help seal the deal.
- Be open to other forms of compensation – There are plenty of non-monetary ways to entice Quants to your company, like having the latest tools, solving challenging problems, organization-wide buy-in for analytics and more. Other things to consider could be flexible work arrangements, remote options or other unique perks.
- Pick up the pace – Talented analytics professionals are rare, and the chances that qualified candidates will be interviewing with multiple companies are very high. Don’t hesitate to make an offer if you find what you’re looking for at a swift pace – your competitors won’t.
- Court the candidate – Just as you want a candidate who stands out from the pack, a candidate wants a company that makes an effort to stand apart also. I read somewhere, a client from Chicago sent an interviewing candidate and his family pizzas from a particularly tasty restaurant in the city. I can’t say for sure that the pizza was what persuaded him to take the company’s offer, but a little old-fashioned wooing never hurts.
- Button up the process – Just as it helps to have an expedited process, it also works to your benefit is the process is as smooth and trouble-free as you can make it. This means hassle-free travel arrangements, on-time interviews, and quick feedback.
- Network – make sure that you know the best of the talent available in the market at all levels and keep in touch with them thru porfessional social sites on subtle basis as this will come handy in picking the right candidate on selective basis
Redesigned Interview Process
In the old days one would screen resumes and then schedule lots of 1:1’s. Typically people would ask questions aimed at assessing a candidate’s proficiency with stats, technicality, and ability to solve problems. But there were three problems with this – the interviews weren’t coordinated well enough to get a holistic view of the candidate, we were never really sure if their answers would translate to effective performance on the job, and from the perspective of the candidate it was a pretty lengthy interrogation.
So, a new interview process need to be designed that is much more effective and transparent – we want to give the candidate a sense for what a day in the life of a member on the team is like, and get a read on what it would be like to work with a company. In total it takes about two days to make a decision, and there be no false positives (possibly some false negatives though), and the feedback from both the candidates and the team members has been positive. There are four steps to the process:
- Resume/phone screens – look for people who have experience using data to drive decisions, and some knowledge of what your company is all about. On both counts you’ll get a much deeper read later in the process; you just want to make sure that moving forward is a good use of either of both of your time.
- Basic data challenge – The goal here is to validate the candidate’s ability to work with data, as described in their resume. So send a few data sets to them and ask a basic question; the exercise should be easy for anyone who has experience.
- In-house data challenge – This is should be the meat of the interview process. Try to be as transparent about it as possible – they’ll get to see what it’s like working with you and vice versa. So have the candidate sit with the team, give them access to your data, and a broad question. They then have the day to attack the problem however they’re inclined, with the support of the people around them. Do encourage questions, have lunch with them to ease the tension, and check-in periodically to make sure they aren’t stuck on something trivial.
At the end of the day, we gather a small team together and have them present their methodology and findings to you. Here, look for things like an eye for detail (did they investigate the data they’re relying upon for analysis), rigor (did they build a model and if so, are the results sound), action-oriented (what would we do with what you found), and communication skills.
Read between the resume lines
Intellectual curiosity is what you should discover from the project plans. It’s what gives the candidate the ability to find loopholes or outliers in data that helps crack the code to find the answers to issues like how a fraudster taps into your system or what consumer shopping behaviors should be considered when creating a new product marketing strategy.
Data scientists find the opportunities that you didn’t even know were in the realm of existence for your company. They also find the needle in the haystack that is causing a kink in your business – but on an entirely monumental scale. In many instances, these are very complex algorithms and very technical findings. However, a data scientist is only as good as the person he must relay his findings to. Others within the business need to be able to understand this information and apply these insights appropriately.
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Good data scientists can make analogies and metaphors to explain the data but not every concept can be boiled down in layman’s terms. A space rocket is not an automobile and, in the brave new world, everyone must make this paradigm shift.
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And lastly, the data scientist you’re looking for needs to have strong business acumen. Do they know your business? Do they know what problems you’re trying to solve? And do they find opportunities that you never would have guessed or spotted?