Data scientists are a scarce resource. Hiring one will become even harder in the next few years as organizations ramp up demand and compete for the best talent. According to IBM, the need for data scientists “will soar by 28% by 2020”, while McKinsey Global Institute says the “US will experience a shortage of 190,000 skilled data scientists by 2018”. In France, the “Big Data Plan” scheduled by the government will create 130,000 new positions by 2019. These figures are good in terms of jobs creation but are at the same time very scary.
How can the world be prepared to have so many talents ready in few years? How can we recruit the best of them? Based on our experience and research, there are four areas where companies should focus their attention.
Challenges and work environment
A company that wants to recruit a data scientist needs a solid and interesting challenge to convince him or her to join. This type of professional will always have many offers on the table and rather than the salary he or she will prioritize the responsibilities that will be given. Interest will increase the bigger the challenge.
A data scientist is someone who loves to discover new things, new orientations and directions. He or she likes “playing” with complex algorithms, being involved in machine learning and artificial intelligence, and helping the company understand and predict customer behavior. Using the latest technology is of high interest and keeping learning curves in constant improvement will be part of his/her decision. Challenging the data scientist on a daily basis is the best way to motivate him or her.
They don’t want to spend time in boring and repetitive tasks that can be done by data integration specialists or technical specialists. “Survey after survey of data scientists have shown that between 70-80% of a data scientist’s time is spent on assembling data. That is not a highly efficient way to use the skill set of a data scientist” says Mike Gualtieri, principal at Forrester. He suggests that it will be better to team the data scientist up with people who know the data and let him or her concentrate on what could be most valuable for the company — running algorithms, doing predictive modelling and making discoveries.
Data scientists also like to be part of a company’s strategic direction, to be the link between data and business and senior management. One of the questions a data scientist often asks is “who will I be reporting to?”. They feel much more valuable if their is strategic and makes an impact.
Business knowledge vs technical knowledge
A data analyst answers questions defined by business. A data scientist, on the other hand, helps business ask
new questions and provides answers through algorithms. Data Mining for Dummies author Meta Brown, in an article published in Forbes, said a common mistake while interviewing a data scientist is spending too much time asking about and testing technical and coding knowledge, and not enough on business experience and results.
Coding and languages are the way to get results. In the end, what really matters is if the data scientist manages to increase the company’s profits.
Understading the way the candidate uses methodological and critical thinking to increase profits and revenue is much more valuable for a company than knowing how well he or she codes. Data scientists are constant learners, so even if they aren’t the strongest in programming, they will always seek time to learn how to improve.
The average annual salary of a data scientist in the US is $100,000 and they know they’re worth it. The problem in most companies is the use of salary grid where the rate for a data scientist isn’t always fixed, particularly if it’s a new role. Most of the time, companies offer the selected candidate a salary that does not match expectations, resulting in the offer being turned down.
Limited supply has placed data scientists in a position of strength and they know it, particularly those who are the best. It reminds us of SAP professionals in early 2000s when freelance consultants used to charge $2,000 per day in Europe. Now they hardly charge more than $600/day.
Last but not least, the recruitment process has to change. Using traditional processes to hire high-level candidates takes way too long. For an executive, it takes an average of 2-3 months to interview, decide and offer a job to the relevant candidate. If we take into account the notice of the candidate — which varies from country to country — the complete process before he or she starts working extends to 5-6 months. The hiring company will be lucky if the candidate doesn’t back out as 50 percent of those in the recruitment funnel will find another job during the process. A solution is to shorten the recruitment process, which is being tested now by one of our clients.
First, HR teams up with someone from technical team who has taken some guidelines from the hiring manager, the head of data science for example. The HR and technical team member will have a weekly meeting about interesting candidates and who are going to be interviewed. The process will use two interviews (one technical, one with the manager/head/ CXO) and a technical assessment. During the interviews, the company makes sure to involve as many interviewers at once, all within 3-4 weeks maximum. Providing weekly feedback to candidates about the status of their applications will also shows your interest in them.
Companies aren’t organized in a way that will make implementation of this process easy, but set it up at least for critical roles such as data scientists as it will efficiently improve your chances to hire the best.
This article was co-written with Patrick Visouthivong, founder of NewGate Talent Solutions, an independent talent acquisition specialist covering the Asia Pacific and specializing in data science talents. The author is the co-founder and counsellor of Caucus Inc. and president of The Engage Philippines. He teaches strategic management in the MBA Program of De La Salle University. He is also an adjunct faculty at the Asian Institute of Management. Send emails to email@example.com.