Summary
Timothy Daley is a Machine Learning Engineer in San Francisco with 15 years of experience building and deploying statistical learning systems for large, heterogeneous scientific and commercial data. He bridges academic rigor and product delivery, moving from postdoctoral work on single-cell and CRISPR screening analysis to leading data science teams that generated novel proteins via evolutionary MCMC and fine-tuned protein LLMs presented at a NeurIPS workshop. At Epic Bio he designed causal trial strategies using external controls and built backend tooling for guide design, and at Affirm he shipped credit decisioning features, A/B experiments, and deployed production models. He holds a PhD in Applied Mathematics and an MS in Statistics, applying probabilistic modeling and nonparametric methods across biology and finance. Timothy is both a hands-on coder and a mentor who has developed widely-used research software (e.g., preseq) and maintains an active GitHub of analysis tools. His work often combines novel statistical methodology with practical engineering to turn small-N biological problems into deployable, decision-ready solutions.
15 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (PhD) Applied Mathematics, Doctor of Philosophy (PhD) Applied Mathematics at University of Southern California
Master of Science (MS) Statistics, Master of Science (MS) Statistics at Tulane University
Stanford University
English, German, Chinese