Michael Li
When it comes to building a data science team, many companies fail at the first step — creating a job posting. These mistakes have been amplified in the age of COVID-19.
The increasing demand for AI and data science experts, driven in part by the pandemic’s economic impact, is showing no sign of abating. Many employers are failing to identify viable job candidates, much less interviewing or hiring them.
What’s the biggest obstacle holding them back? In our experience, it is often a poorly drafted job posting. And with the pandemic completely stopping all in-person recruiting events, hiring success hinges on an effective job rec. Previously tolerable mistakes are now fatal.
At The Data Incubator, a data science training and placement firm, we’ve helped hundreds of companies successfully hire data science teams. Honestly, it pains me to see amazing companies undersell themselves in this area.
Companies inevitably gravitate toward the same generic buzzwords, promoting themselves as “cutting edge,” “creative,” “collaborative,” “data driven,” “passionate” or “insightful” (just peruse Indeed for examples of these lackluster postings). Or they delve into industry jargon, which may be lost on candidates who are not familiar with the industry.
To streamline the writing process, we recommend that clients break down their competitive advantage into three buckets: compensation, mission and tech. Only by understanding where their strength lies can they successfully market their job openings.
Compensation
Compensation is an important component of making a position competitive. Managers certainly need to fight to ensure their remuneration range is appropriate for their data science roles. However, budget constraints are difficult to overcome, especially given the ability of tech and finance to pay top dollar for these sought-after skills. How to combat this when you don’t have the same budget? Consider listing compensation in job ads.
If you’re one of (the majority of) employers who cannot afford to compete on salary, this will help job seekers understand what to expect. Neither you, nor a potential candidate, wants to spend hours interviewing just to discover that it would have never worked out because of compensation. Save yourself the time and frustration by listing remuneration upfront.
What if you are one of the few employers able to pay major-league salaries? Congratulations, but don’t throw away your hard-won budget! Companies develop reputations for compensation. Unless you are one of the select firms with a reputation for paying top dollar, you will need to signal that to top talent. Otherwise, strong candidates may assume the remuneration is low and not apply, defeating the purpose of paying a high salary in the first place.
Obviously, listing salaries is controversial and there are plenty of reasons why employers are weary of listing salary ranges. However, a recent survey by SHRM found that 70% of professionals want to hear about salary upfront and Glassdoor.com reports that salary is the No. 1 consideration for 67% of job seekers. With all these benefits, employers should seriously consider being more upfront and transparent about what they are able to pay, if only to save themselves time and frustration.
Mission
In the COVID-19 workplace, employees are finding themselves increasingly isolated. With work from home poised to stay even after the virus has dissipated, the risk of isolation will continue. Companies need to double down on articulating their mission and galvanizing employees around that. This doesn’t just start with employment but the very first step of the hiring process: the job posting. Emphasizing mission in the job posting will attract employees.
Indeed, purpose-oriented employees are 64% more fulfilled and Deloitte reports they have 40% higher levels of retention. Selecting the right mission-aligned candidate can be more impactful than trying to motivate ones who are not aligned with the mission.
Case in point when a hiring manager in life sciences asked me to look over her job rec for a data science position she was offering. She had been trying to hire for months without much success. The initial job position was written by HR. It was short on detail but replete with antiseptic platitudes about how the position was a “unique opportunity to change lives” and the “diverse and inclusive” work environment provided “professional growth opportunities,” “outstanding benefits,” “unique resources,” “intellectual excitement,” and ample opportunities for “creative problem-solving.”
Those are all wonderful qualities in any workplace, but they are vague and overused. We are all accustomed to skimming such corporate bromides, leading to a double loss: The employer missed a valuable opportunity to pitch itself and potential applicants missed the chance to learn about a truly amazing opportunity.
Working together, we revised the draft, adding in a brief description about the fascinating science behind the work and the potential impact on hundreds of millions of patients worldwide. We expanded the sections on machine learning, advertising the unique datasets to which candidates would have access. Most of all, we eliminated the corporate clichés and scientific jargon to make room for takeaways that appeal to data scientists. The end result was a pithier job rec that received far more qualified interest and resulted in a hire.
Tech stack
Data scientists, like many other technical people, are driven by the desire to improve their skills. Indeed, most think of themselves as craftspersons (online marketplace Etsy’s engineering blog is appropriately entitled “Code as Craft”). Employers will attract talent by highlighting the skills candidates will develop while working with industry-leading tools and proprietary data.
Data scientists are propelled not just by idle trend chasing. Developing skills in the right technologies can be lucrative. Candidates are mindful of their next hire and want to learn transferable skills widely valued in the industry. They will be far more likely to take a job that uses Tensorflow, a popular open-source deep-learning library from Google, than less popular competing tools.
Incidentally, ZipRecruiter reports that the national average for jobs that require Tensorflow pay $29,000 more than generic data science positions. It is important to recognize that the choice of technology stack directly impacts how competitive your job recs can be. And if you have adopted the “must-know” tool of the year, be sure to advertise it.
Open source plays another huge role in attracting job candidates. The data science ecosystem is built largely around open-source tools. Data scientists don’t want their productivity tied to an expensive software license and inflexible closed tools that they cannot easily tweak. The best companies don’t just use open source, but share their technology and tooling as open-source packages (like Google did with Tensorflow). Data scientists are attracted to these companies for altruistic and selfish reasons.
Altruistically, data scientists genuinely want to give back to the community that has enabled their entire field. Selfishly, open source represents one of the few ways they can highlight their skills while working for the company, given that the majority of their code and analysis will be hidden. Whatever the motivation, top data science talent will seek out companies that have a strong open-source culture.
Companies should emphasize (in job recs) their contributions to open source and the opportunity for candidates to contribute to open-source projects. They should also go one step further and actively maintain their Github page to showcase their open-source contributions and demonstrate their commitment to the community.
In our experience working with hiring managers, they are almost all worried that they do not have the caché of a Google, or the competitive compensation of a well-heeled hedge fund. But as we have seen, mission and tech can be as powerful a motivator as compensation. Providing details (particularly falsifiable facts) rather than weaselly platitudes will go a long way in demonstrating the strengths of your job offering.
Oftentimes, HR professionals do not feel comfortable articulating the technology stack of the company (and sometimes even the mission as well, especially if the company is in a technical space). HR professionals need to collaborate with data science managers to craft job descriptions that speak to a data science audience. The increasingly pivotal role of job postings will not abate with the virus. The decentralized workplace is here to stay and with it, virtualized hiring and the central importance of a strong job rec.
Companies need to master job recs to stay ahead of the increasingly fierce competition for global data talent.
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