Daniel Fernandez Silva
Daniel Fernandez, Head of Data Science at Freestone Grove Partners
Daniel Fernandez is a data scientist specializing in the research, application, and deployment of machine learning and statistical methods at scale across diverse domains, primarily healthcare and finance. As the statistician John Tukey famously said, “The best thing about being a statistician is that you get to play in everyone’s backyard.”
Daniel’s Ph.D. research focused on Bayesian statistics, hierarchical models, and MCMC methods, with applications in healthcare and genomics. He also worked as a cancer genomics and epigenomics researcher and developer at Massachusetts General Hospital (MGH) and the Broad Institute.
After completing his Ph.D., Daniel joined Palantir Technologies as a Computational and Mathematical Engineer. There, he developed and deployed data science software and collaborated with clients across multiple sectors. His main areas of focus included economic and financial estimation and forecasting using transactional data, as well as predictive maintenance leveraging IoT and sensor datasets.
Daniel later led data science and technology at Encompass Capital, a long/short equities sector-focused hedge fund based in New York City, specializing in mining, industrials, and energy. Following Encompass, he worked as a research scientist at Wellington Management, where he applied AI and recommendation systems to venture capital—optimizing the investing process from discovery and screening to due diligence and deal-making. He remains active in the VC community.
Currently, Daniel is Head of Data Science at Freestone Grove Partners, a long/short multi-strategy equities hedge fund founded in 2023 with offices in San Francisco, New York, Palm Beach and Austin. Freestone invests across all major sectors through specialized industry expertise and rigorous fundamental, bottom-up equity research, combined with quantitative disciplines such as risk and factor modeling, portfolio construction, and data science. The data science team operates as a research and embedded function, working directly with investors to generate alpha through the application of industry- and company-specific alternative datasets and forecasting models tailored to distinct economic and fundamental drivers. In this role, Daniel leads the development and deployment of quantitative models across the firm’s six core sectors: TMT, consumer, natural resources, healthcare, industrials, and financials. He frequently works with datasets exhibiting strong autocorrelation, such as time series and geospatial data.
Education
- Ph.D., Statistics, Harvard University
- M.A., Statistics, Harvard University
- M.S., Industrial Engineering, Pontificia Universidad Catolica de Chile
- Bachelor of electrical engineering, Pontificia Universidad Catolica de Chile