Kevin Stumpf
Co-Founder and CTO
Tecton
Panel: What Do Engineers Not Get About Working with Data Scientists?
apply(conf) - May '22 - 30 minutes
ML is increasingly making its way into production to power customer-facing applications and business processes. This transition from batch to operational ML raises new organizational challenges. Data scientists and engineers now have to work collaboratively as a single team. This requires adaptation on both sides – combining data science and engineering processes into a well-integrated MLOps machine. Our panel of data scientists will provide their perspective on how data engineers can support this transition and more effectively work with data science teams.
Kevin co-founded Tecton where he leads a world-class engineering team that is building a next-generation feature store for operational Machine Learning. Kevin and his co-founders built deep expertise in operational ML platforms while at Uber, where they created the Michelangelo platform that enabled Uber to scale from 0 to 1000's of ML-driven applications in just a few years.
Prior to Uber, Kevin founded Dispatcher, with the vision to build the Uber for long-haul trucking. Kevin holds an MBA from Stanford University and a Bachelor's Degree in Computer and Management Sciences from the University of Hagen. Outside of work, Kevin is a passionate long-distance endurance athlete.
Maria Cipollone is an experienced user researcher, who can gather insight from both qualitative and quantitative methods. She uses design research practices to improve internal tools at Spotify, notably for data scientists and machine learning practitioners.
Mark is a community health advocate turned data scientist interested in the intersection of social impact, business, and technology. His life’s mission is to improve the well-being of as many people as possible through data—especially among those marginalized.
Kate Kuznecova
Data Scientist
OLX Group
Kate is a data scientist at OLX Group where she works in a team that provides data infrastructure, services and products for the entire group. Coming from a consulting background, she's worked with teams of varying maturity and domains ranging from Healthcare to Gaming. She believes that the key to successful ML projects is a strong data strategy, relentless communication and targeted people development.
Outside of her core work, she co-founded Riga Data Science Club and led the analytics track at Riga Tech Girls program to help more people develop data skills in her native Latvia.
Alice did a degree in physics, with a final masters project in data science disguised as particle physics. After an accidental year in finance, she found her way into the data science programme at Channel 4, she and another grad went on to build that team from two to twelve. From there she worked at Expedia, and is now the Head of Data Science and Machine Learning at Depop, where the team work across Ranking, Recommendations, Performance Marketing and Trust.
