Sam Fisher is a data scientist with a decade of experience translating regulatory complexity into practical, auditable analytics for banking and fintech. At Stratyfy he built interpretable fairness metrics, attribution methods for disparate impact, and consumer-facing remediation suggestions while contributing to a Stanford+FinRegLab research study on ML explainability in credit underwriting. He blends strong production ML skills—scikit-learn pipelines, numpy, custom control algorithms for sales optimization—with a background in NLP, unsupervised learning, and matrix factorization from consulting projects. Sam’s career spans startups, freelance engagements, and product leadership roles where he shipped anomaly detection, voice integrations, and automation that materially improved business outcomes. Trained through MITx, Harvard Extension, and Springboard, he pairs formal statistical training with hands-on engineering and a creative past in music and sound design that informs his quantitative intuition. Based in Little Rock, he is particularly adept at turning ambiguous regulatory and product requirements into measurable, operational solutions.
10 years of coding experience
3 years of employment as a software developer
Bachelor of Music, Technology in Music and Related Arts, Bachelor of Music, Technology in Music and Related Arts at Oberlin College
Master of Liberal Arts, Data Science, Master of Liberal Arts, Data Science at Harvard Extension School
Data Science Career Track, Data Science Career Track at Springboard
MicroMasters, Statistics and Data Science, MicroMasters, Statistics and Data Science at MITx on edX
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.