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The Human Algorithm: Automating Startup Data Collection at Mattermark

Sarah Catanzaro heads up the Mattermark data team, where she leads a team of three analysts partnered with 3 machine learning engineers to collect and clean massive amounts of company data. Last week, she spoke at the RJMetrics Datapoint Live conference to share a bit more about how we achieve scale through automation. You can also view the video of her keynote here.

By focusing on the real questions people ask, and building the data set to answer the most important questions first, we go beyond aggregation to what we believe really makes what we offer special and unique: workflow.

Data wants to be free, and most of the data we organize can be found publicly on the Internet if you’re willing to do enough digging and spreadsheeting. We fundamentally believe our customers want to spend their precious time on more important things, things only humans can do: meeting with other people. Whether an investor, sales person, executive or other deal-maker we believe data is only a jumping off point for building new and better informed relationships.

Machine Learning Engineers + Data Scientists

With such a strong emphasis on automation, it should come as no surprise that the team tasked with solving these problems is highly specialized and comes to us from companies where data matters: Palantir, LinkedIn, Amazon, etc. To learn more about how we tackle the software engineering challenges presented as we organize the world’s business information check out this talk from Mattermark machine learning engineer Samiur Rahman.

Introduction to Neural Networks, Vector Reduction and Natural Language Processing

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© Mattermark 2024. Sources: Mattermark Research, Crunchbase, AngelList.