Hi, I’m Jeff! Based in Stockholm, Sweden, I’m a computational researcher and machine learning executive with a career spanning over 35 domains. My journey has taken me from pioneering fire prediction systems at the NYC Fire Department and advising on climate data science at NASA to developing personalized streaming experiences at Viaplay in the EU. I enjoy helping organizations evolve their work by giving a grounded perspective on AI, machine learning and statistics. As such, I’ve had the pleasure to collaborate with a wide range of professionals such as macroeconomists, climatologists, soccer coaches, media executives, and software engineers.

For the past seven years, I’ve been immersed in Stockholm’s thriving tech scene while balancing the joys of raising my kids. Currently, I am the Head of AI at Combient, where I’m building the Combient AI Center that is focused on normalizing effective, human-centered AI across a network of 37 of the Nordics’ largest companies. We’re a group of senior experts in applied science, engineering and anthropology figuring out how to make agentic and generative AI in the network of companies. My previous roles in Sweden very different domains including Chief Data Scientist & Director at Aira Group developing intelligent heat pump and residential power optimisations systems as well as SVP for data and machine learning at the streaming service Viaplay.

Originally from the U.S., I was among the earliest data scientists to work in public service, leading applied R&D that has led to scaled impact such the NYC Fire Department’s FireCast  — a fire prediction system for risk inspections – as well as data-driven hurricane response at the NYC Mayor’s Office during the Bloomberg Administration. These experiences shaped my perspectives on integrating science with policy and strategy of large organisations.

Later, I joined the U.S. Federal Government to help build data science and ML capacity, starting at NASA  and the Obama White House. I later served as the first Chief Data Scientist in a US cabinet agency (U.S. Department of Commerce) and as the Chief Innovation Officer of the U.S. Bureau of Economic Analysis. One of my most memorable experiences was designing and building out ML models that directly helped the accuracy of the US’ Gross Domestic Product.

Outside work, I’m constantly learning and building. I co-authored Data Science for Public Policy (Springer Nature) with Professor Ed Rubin and Bayesian Econometrician Gary Cornwall. I’ve taught data science at Georgetown University’s McCourt School of Public Policy, advised startups like Urbint, consulted for DC United (Major League Soccer), and advised and built algorithms for the UN Development Programme in Nigeria.

I hold a Bachelor’s in Economics from Tufts University, a Master’s in Quantitative Methods from Columbia University, and I’m currently pursuing a Doctorate in Computational Statistics and Machine Learning at the University of Oxford with Daniel Wilson and Anders Kock.