As you search for candidates to build your AI team, think of the process like dating: they’re in high demand, and you’ll have to work to attract the right match. You’ll also have to contend with big players like Google, who snap up data scientists as soon as they step out into the sunlight. Most companies make the wrong moves in this workforce dating game. Think of the following suggestions as a guide to attracting your perfect date.
You should not try to skimp on the salary. This is a fast way to fail—don’t cheap out. If you’re going to invest, invest. If you don’t have what it takes to invest, then don’t engage with data science.”
Senior Data Scientist, Nike
When an executive knows that all data scientists are just ‘wicked smart,’ you’re already set up to fail. So, that’s where I set business expectations—what [data scientists] can or cannot do. But if they walk in thinking that [I’m] going to walk on water, typically that’s a red flag to me.”
Principal Data Scientist, American Family Insurance Client Services
The average postdoc salary in the US is about $60K per year. A few years ago it wasn’t hard to convince someone who had a physics PhD to forgo the poor pay and academic headaches to be your data scientist for two or three times the salary. But those days are long gone. Data science salaries have been going bonkers for the last few years. A data scientist can often change jobs and increase their compensation by 30% or more. It’s just supply and demand.
To build your data science team, you have three choices:
We hope you’ll be smart and choose the third option. Competitive compensation is table stakes; you don’t have to beat all market salaries to build your team.
Data scientists want to work for leaders who have more experience than they do. This insight is critical for your organizational planning.
Many companies struggle to decide whether to centralize or distribute their data science talent. But realistically there’s only one option: centralize your data scientists and let them matrix into project teams.
Too many companies begin their AI journey by hiring a data scientist into a traditional software team. After a few months of trying to explain why the data science experimental process doesn’t align with the sprint release cycles, the data scientist quits. Unless you change your structure, you’ll have difficulty recruiting and retaining data science talent.
To make your job descriptions more effective, highlight your hiring manager’s understanding of the data science workforce and role. A rewritten job description can instantly lift the quality and quantity of candidates. For an example, see the “Resources” section at the end of this report.
In terms of seniority . . . I observed that the higher you are, the less hands-on work you do. I want to go to the next level, without management responsibilities, while still being able to actually do hands-on work.”
Staff Data Scientist, Intuit
Fewer than half of the data scientists we surveyed have a strong interest in becoming a manager. Most were only somewhat interested in pursuing career growth through a management path.
Unfortunately many companies don’t offer an alternative to the management path. If management is the only available advancement option, many data scientists will leave so they can continue doing data science.
Offering promotion alternatives is a no-brainer. You can start doing it today.
The complete guide for understanding AI, identifying opportunities, and launching your first product and become an AI Company in 90 days.