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Let the data-scientist arms race at venture capital firms begin.

Principal James Wise of Balderton Capital, a firm that makes early-stage investments, announced today it has brought on Ferenc Huszár, who previously was senior data scientist at social-media analytics startup PeerIndex.

And another early-stage venture firm, General Catalyst Partners, is seeking a data scientist/associate.

These hires represent the latest escalation of venture capitalists’ interest in making more data-driven decisions.

Investors have different ideas about how they want to put data scientists to work. An in-house data scientist at Balderton, for example, could help partners give better feedback to their portfolio companies as they consider new ideas based on the experiences of other startups the firm backs.

“What we’re trying to do is use that proprietary insight along with public data to make better real-time decisions,” Wise said in an interview with VentureBeat. “It’s impossible to do that without having that special perspective.”

Investors have long made decisions about whom to fund based on subjective criteria, like how much they believe in the people behind startups. But more are looking for objective input — and not just early adopters of data analytics like Google Ventures and Correlation Ventures or firms that have added data scientists in residence, like Accel Partners, Greylock Partners, and IA Ventures.

(VC firms aren’t the only companies taking a more data-centric approach. We’ll discuss how many companies are leveraging “big data” at VentureBeat’s upcoming DataBeat conference, May 19-20 in San Francisco.)

We aren’t at the point when every VC firm is bringing on a data science team, although the General Catalyst opening and the Balderton hire do suggest that such a day could come in the next few years.

“I imagine more VCs will move in this direction, but I don’t think it’s going to be all-encompassing,” Wise said. “I think it’s going to be another string to the bow, rather than being the whole bow.”

Venture investing won’t become a matter of relying on a single master algorithm or a data-science application that analyzes ranking in app stores and changes in social media activity. It’s more about augmenting the value of investors.

A data scientist could also help VCs with necessary work, like finding startups to review and conducting due diligence.

“Data science and analytics techniques are, I think, applicable to both of those,” Donald Fischer, a venture partner at General Catalyst, said in an interview with VentureBeat.

General Catalyst has analyzed data in the past, but the new role “is sort of new in some respects because there’s a set of, you know, core data-science skills that we’re looking to bring into the firm,” Fischer said.

It’s an interesting step for General Catalyst, which makes a point of being “entrepreneurs investing in entrepreneurs” on its website. Data science capabilities will complement, not replace, the firm’s “human-centric” approach, Fischer said.

Investors at major firms say venture investing still happens based on the old ways, with investors relying on instinct, relationships, and deep knowledge of industries. They disabused this reporter of the notion that every VC firm is hiring data scientists by the dozen. Still, things are moving in that direction.

“At the most basic level, I think you can observe that all businesses are sort of getting more analytics driven, especially in the technology realm, you know,” Fischer said. “It’s a critical, central focus of many of our portfolio companies, both on the consumer side and our e-commerce companies are obviously all highly data- and analytics-driven, increasingly on the enterprise side as well.”

Then again, VC firms could simply make data scientists into full-blown investors instead of people with separate jobs.

“I actually think some data scientists will actually just become VCs vs. just ‘data science’ pegged roles,” Shivon Zilis, a venture capitalist at Bloomberg Beta, wrote in an email to VentureBeat. “They like novel problems and keeping on top of many problems/projects. They often have both technical and product sense. My guess is they have good instincts on investing on average but, then again, that’s an untested theory.”

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