Tomasz Tunguz
Venture Capitalist at Redpoint
Which of the Five Types of Data Science Does Your Startup Need?
Credit: O'Reilly
Startups, you are doing data science wrong. That’s the title of a post penned by Ryan Weald in GigaOm this week. Weald echoes DJ Patil’s idea: “product-focused data science is different than the current business intelligence style of data science.”
Weald points to a different model of data scientist, an engineer, not a statistician, who can perform queries and based upon some insights, improve the product with a few code changes and a push to git.
I like Weald’s post but disagree on one point. I don’t think there is one type of data scientist, but five.
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Quantitative, exploratory data scientists tend to have PhDs and use theory to
understand behavior. I count Hal Varian, Chief Economist at Google, and
Redpoint’s own Jamie
Davidson, among them. Varian’s team researches the advertiser dynamics
within the ads auction and compares those dynamics to theoretical auction models
like theVickery auction. By combining theory and exploratory research,
these data scientists improve products.
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Operational data scientists often work in the finance, sales or operations
teams at Google. In the AdSense ops team where I started, we had a star data
analyst who each week would discuss our team’s performance: our email response
times, the satisfaction scores of our publishers, and changes in publisher
behavior by segment. His work provided a feedback loop to improve the team’s
tactics and efficiency. Only infrequently were these insights used to influence
product.
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Product data scientists tend to belong to product management or engineering.
This is the group of data scientists Weald writes about. PMs and engineers sift
through logs and analysis tools to understand the way users interact a product
and leverage that knowledge to refine the product. At Google, the ads quality
team analyzed user clicks data to improve ad targeting.
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Marketing data scientists segment the user base, evaluate the performance of
advertising campaigns, match product features to customer segments, and design
content marketing campaigns. The marketing data scientist creates awareness and
leads for the sales team, helping generate revenue.
- Research data scientists create insights as a product. Nate Silver is arguably the most famous of them. Silver’s work doesn’t influence a product; the analysis is the product itself. Sometimes the data science leads to a thought leadership whitepaper, or a blog post, or a financial report. It’s rarer for startups to employ research scientists because the output isn’t tied to revenue. But larger companies like Google do, think tanks do, financial institutions do.
To talk about data scientists might be too myopic. Your startup may need a research data scientist or one with a PhD. Or it may need an engineer with an understanding of basic statistics who can work up and down the Rails stack. Or another type all together.
Like any role, when hiring or recruiting a data scientist it’s important to identify what the key problems facing the business and the relevant skills the right candidate will need to solve those challenges.
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