dear advisor: Who should be my first data hire?
Or: When should I hire a Data Scientist?
Having collaborated with companies of all shapes, sizes and industries -- on how to use data to improve the product and have happier customers for the past 10 years, I’m here to tell you that your first hire should not be in Data. :)
Since technology and data will be core to your business, I recommend building out your team in phases, with alignment on different end goals.
Phase 0 end goal: Understand how you're bringing value to your customers
Before diving into technology and data, you need to iron-out who your target customers are, how you'll be brining them value, and how you plan to make money from the service
Phase 1 end goal: POC to evaluate if there’s value in your data as early as possible
Since you can't get insight from data you didn't collect, start with a (Fractional) CTO or Data Architect, who’ll be able to advise you on pros and cons of your technology stack -- and where to store the data
They can then help you hire a Back-end Developer, if you don’t already have one, to help you begin collecting the data about your business and customers
Then bring on a (Fractional) Chief Data Officer, to help you and your company:
Validate and advise on data as it's coming in,
Prioritize and identify the most impactful product direction or business questions, and
Develop a proof-of-concept (POC) to try to answer this question -- and bring value to the business (based on the data available), which will also help you
Please note: There are no guarantees that we may actually be able to answer this question, because we don't know ahead of time what data is/isn't there or what the quality of the data is; but, even if we won't it will inform us on what step to do next.
Learn from the process and uncover any gaps, quality issues in the data collection, or any other constraints/considerations.
Phase 2 end goal: POC to understand who your customers are now and historically
Your team from Phase 1 is in place.
Depending on what you learn from the POC about your customers and the data quality, discuss what it would take to bring business value from data, if at all.
If there is potential business value from data, collaborate with the fractional CTO and CDO to determine if the next hire should be a mid- to senior-level Data Analyst or Data Engineer -- and have them help you hire for the right role.
Mid- to senior- level data professionals with (ideally) expertise in your industry and tech stack will also be able to recommend initiatives based on business impact, not just execute tasks.
Have the new hire, with advising from CDO, develop a POC to understand who your customers are now and historically.
Please note: Until the business is able to understand what’s happening now and historically, please hold-off on hiring any Data Scientists to help you make forecasts.
What happens next?
ideas for sample POCs here
Who should first data hire report into? David Murray’s lessons learned from re-structuring his data team(s)
Are you looking for a fractional CDO to collaborate with on a POC, to help you evaluate how to improve and grow the product? Please reach out.
Are you getting enough applicants? Do you need to improve your job description? Please schedule this flat-fee session.
You may also like:
Dear Advisor: I'd like to make data-driven decisions. Where do I start?
Dear Advisor: How do I avoid the biggest data/AI/ML mistakes others make?
Advice for systematizing Product Market Fit from the very beginning
Different perspectives on who should be your first hire:
Article by Mengying Li for First Round Review suggests having a technical challenge as part of the hiring process (which I disagree with)
Blog by Andrew Bartholomew suggests hiring someone senior full-time
Advice for structuring and aligning your Data Team:
Scaling Data: Data Informed to Data Driven to Data Led, by Crystal Widjaja -- on who should be the first hire, how to align the team and how to scale it
How should our company structure our data team?, by David Murray
Models for integrating data science teams within companies: a comparative analysis, by Pardis Noorzad
Blitzscaling a data team: from 0 to millions, by Ariel Wolfmann