Data scientists at work.

Hiring a Data Scientist? Don’t Make This Critical Mistake

Matt Hertig Alight Insights

Data scientists at work.

Here’s something I hear a lot: “I’m starting up a marketing analytics team, but I’m having a really hard time hiring a data scientist.” 

I’ll ask about the rest of their team. More often than not, they’ll say, well, there’s nobody else on the team yet. This would be our first hire. 

And that’s the wrong way to staff a marketing analytics practice. Your data scientist should be one of the last people you bring on board. 

I like how this guy put it: 

Marketers don’t know what they’re asking for when they ask for a data scientist. It’s the Wild West, especially if a company has never dealt with data. 

Companies that aren’t ready for a data scientist, hire one and end up wasting $100,000 a year. Most small businesses don’t need a data scientist; they need someone to handle a spreadsheet or a data analyst.



The Right Time for Hiring a Data Scientist

Successful marketing analytics teams tend to have people in six roles. Here’s a good rule of thumb: If you’re spending $1 million or more annually on marketing, you should have someone in each of the following positions, in this order: 

  1. A strategist, someone who uses data to make decisions about media spend and marketing strategy
  2. An analyst, someone who tells the performance story by creating reports (ideally in the form of a marketing dashboard) and generating insights
  3. A data engineer, someone who oversees connections to data sources and creates unified datasets
  4. A solution designer or architect, who plans for the long-term scalability and efficiency of your analytics
  5. An influencer, someone — usually a CEO or another senior leader — who acts as an advocate for analytics and helps secure the necessary resources, whether that’s budget or access to data
  6. A data scientist, someone who discover build models and uncover unexpected trends in your data

The data scientist is last, but not because they aren’t important. Data scientists are absolutely critical. 

The problem is that, when you hire a data scientist first, without filling all those other roles? Your data scientist gets stuck doing the job of an analyst, data engineer and solution architect. Instead of building models, your data scientist is busy building reports and babysitting data connections.  

Data scientists have to figure out all that other stuff before they can do the job you hired them to do. Most of their time will go to data prep and management. Not only is that a waste of money (the average salary is about $96,000), it’s a recipe for higher turnover.  

And that’s not great given how expensive and difficult it can be to find data scientists. As Gartner reported: 

Furthermore, evidence from Gartner’s Marketing Data and Analytics Survey 2018 indicates investments are mismatched with their output: Expensive and talented data scientist resources are often mired in basic, operational work.

This presents a significant risk to the ongoing investment into data-driven marketing. Business cases for funding rely on a perception that data and experimentation will transform marketing. Failure to deliver against inflated expectations may come at the expense of future funding commitments.

Set Your Data Scientist Up for Success

Data scientists are important resources, and rightly so. They understand data, and they understand technology, and they know how to bring them together and uncover patterns you can’t see. 

But they shouldn’t be your first hire. Build your team in the order outlined above. That way, when you do add a data scientist, they’ll be free to operate at the highest possible level of performance. 

And by the way, hiring a full-time data scientist might not be necessary. 

Contracting out that piece of your marketing analytics could make more sense for your organization. Full disclosure, that’s Alight’s model. We offer Analytics-as-a-Service, providing all the people, technology and support that marketers need to produce reporting and insights. 

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