This summer, Identified pulled back the curtain on a new artificial intelligence technology, called “SYMAN,” which it developed to help organize and clean the swaths of unstructured professional data that today lives on the Web. In doing so, the startup moved away from its original focus — a kind of Facebook data-driven “Klout score for professionals” — essentially admitting that if it were going to give job seekers and companies a better way to connect and find talent, it would have to address the elephant in the room: Data.

To deliver a quality search or analytics product that could compete with LinkedIn (and actually produce some actionable insight), Identified teamed up with a group of former LinkedIn data scientists to help clean social media data with SYMAN and then productize it to enable customers to power their recruiting, human capital management, CRM and others. After beginning its mining and cleaning operation with Facebook’s professional data, the startup now has the help of one of the lead data scientists at Facebook, whose job it was to craft strategy around that very dataset.

Today, the company officially announced the hiring of Mohammad Sabah, the former Manager of Data Science and Analytics at Facebook, who will be joining Identified to lead its data science and engineering team as “Chief Data Officer.” At Facebook, Sabah led data science and analytics for the “Identity Team” at Facebook, where he was responsible for building the team and developing strategy for key products like Timeline, Profile Completeness and Privacy.

Essentially, the Identity Team owns and cultivates Facebook’s most valuable asset: Your personal data — and the data generated by the 1.2 billion people (and their relationships) on its platform. Sabah and his team tackled the challenges of deciding what are the most relevant stories that should be surfaced in your Facebook profile’s Timeline and figuring out ways to encourage Facebook users to complete their profiles, increase accuracy of those profiles and so on.

Before joining Facebook, he was a principal data scientist and engineer at Netflix, the Rubicon Project and Yahoo, where he focused on large-scale machine learning algorithms to improve personalization, search, click prediction and keyword recommendation — among other things.

Identified began its data mining by targeting the healthcare industry (and jobs in medical fields) in particular, and Sabah joins Identified to help develop the company’s patent-pending tech, build a team of data scientists around it and help apply it to new industries.

Prior to launching SYMAN earlier this summer, the company had remained fairly quiet over the last nine months as it pivoted in this new direction and began hiring a team of data scientists that would help lead the way. While Facebook may not seem like the go-to source for professional data (usually, that’s LinkedIn’s expertise), the co-founders point out that its dataset is actually five-times the size of LinkedIn but far less complete — and structured. Plus, it helps that there are over one billion people using the social network today.

So, essentially, the data architecture they’ve developed since enables machines to more quickly draw inferences about Facebook’s data entries based on context, natural language and a host of other signals. Knowing that someone has listed their title as “Analyst” on Facebook isn’t much good to a recruiter, however, if, by digging into context and related data, SYMAN can help hiring managers deduce that the person is actually a “Systems Analyst” at Salesforce, then you have something recruiters would probably be willing to pay for.

Identified began testing SYMAN with around 30 companies, including enterprise health clients like Kaiser Permanente, but is now in the process of expanding to target other industries — beginning with consumer goods — and its list of clients has grown to 58 since May. The company has raised $22.5 million to date from investors like VantagePoint Capital, Capricorn Investment Group, Tim Draper, Innovation Endeavors, Chamath Palihapitiya and others.

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