Notch, Richmond’s fast growing data agency, announced today that it will join Capital One as part of the Center for Machine Learning. An in-house consultancy and center for product delivery, the Center for Machine Learning helps millions of customers at Capital One with their financial lives. Notch has moved its cross-functional team – ranging from machine learning experts with advanced degrees to data engineers – to Capital One’s West Creek campus.
Prior to acquisition, Notch earned media headlines and industry buzz for helping its clients adopt real-time, distributed platforms and machine learning to change the way they leverage their data. Since its launch in 2014, Notch has delivered nearly 70 projects to 34 clients—ranging from early-stage startups to Fortune 100 companies.
The company’s founders, David Der and Paul Hurlocker, have held chief technology officer roles in previous high-growth companies and have both helped launch multiple startups in the technology industry. Since creating Notch, Der and Hurlocker have attracted others who are equally passionate about the Cloud, big data, machine learning, and reactive applications with the goal of serving customers more effectively.
“We bootstrapped Notch ourselves, gaining traction quickly because there are few companies that have our real-world expertise,” said Paul Hurlocker, CEO and co-founder of Notch. “We are proud of the fact that we helped move established organizations forward and assisted in the launch of startups through our work.”
Over the past year, Notch began working closely with Capital One and it quickly became clear that there was a natural fit for the Notch team. “We found that Capital One operates with the innovative mindset and skills of a top technology firm, but with the scale and expertise of a leading bank,” said Hurlocker, who has taken the role of senior director of software engineering at Capital One. “Our teams come to work every day to collaborate with other talented engineers and data scientists to solve interesting and challenging problems while pushing the adoption of machine learning in diverse, new, and impactful ways to drive business value. And we’re both aligned in our belief that actively promoting continued learning and development will keep us on the cutting edge of machine learning and data engineering capabilities.”
Richmond Grid will continue to follow the Center for Machine Learning story and update readers on the new team’s efforts to change banking for the better and to deliver technology that improves customer experiences.