Daniel Fernandez is a data scientist with a focus on researching, applying and deploying machine learning and statistics at-scale in a wide range of disciplines, mainly healthcare and finance. As the famous Tukey once said: "The best thing about being a statistician is that you get to play in everyone's backyard."
Daniel's research during the Ph.D. was in Bayesian statistics, Hierarchical models, and MCMC methods and its applications to healthcare and genomics. He was also a cancer genomics and epigenomics researcher and developer at MGH and the Broad Institute.
After his Ph.D., he worked as a Computational and Mathematical Engineer at Palantir Technologies. He was responsible for developing and deploying data science software as well as working in data science applications with several clients across multiple sectors. His main areas of application were economic and financial estimation and forecasting using transactional data as well as predictive maintenance using IoT/sensor datasets.
Daniel then worked in data science and technology at an L/S equities sector-focused (mining, industrials and energy) Hedge Fund based in NYC. He most often works with datasets that exhibit strong auto-correlation such as time series and geospatial datasets.
- Ph.D., Statistics, Harvard University
- M.A., Statistics, Harvard University
- M.S., Industrial Engineering, Pontificia Universidad Catolica de Chile
- B.E.E., Pontificia Universidad Catolica de Chile