Data Scientist Websites

Chris Loy is a software engineer, data scientist, and serial entrepreneur working in the AI space. His background is in engineering and data science. He was the CTO and co-founder of Datasine, a startup in the AI space that was acquired by Shutterstock. Currently, he works as a startup advisor and fractional CTO. Chris has experience writing code, training machine learning models, building software products, and running tech teams. He has worked at companies big and small, mentored junior engineers and data scientists, and served as an advisor for some companies. He writes about tech, data, and AI on his website. He is based in London.

Jeet Mehta is the founder of Swift, a B2B SaaS company for sports facilities. Previously, he worked as a Product Manager & Data Scientist at Shopify. He was born in Bangalore and raised in Toronto. He has experience in mobile development, circuit design, machine learning, data science, and product management. He played for the Canadian national cricket team. Jeet enjoys reading, writing, and playing cricket. He is interested in building an online presence and publishing written content.

Jon is a person who maintains a website featuring a blog. He has a Github and a resume. His blog posts cover a range of technical topics, including comment systems, online advertising, databases like DuckLake and TimescaleDB, PubSub with PostgreSQL, application building with Go and SQL, Kubernetes, Apache Airflow, Observable Plot, Python geographic plotting, and JWTs in Go. He also writes about the adapter pattern in Go.

Edward Ross is a Data Scientist, Writer and Learner. He is the creator of Whatcar.xyz, a Deep Learning classifier for Australian cars, and a system for extracting and cleaning Australian job ads from Common Crawl. He writes articles on skeptric.com covering topics such as calculating Moving Averages in SQL, Searching Common Crawl with CDX and Athena, Finding near duplicates with Minhash, and Calculating the Centroid on a Spherical Polygon. He has also worked through tutorials and books such as Computational Exercises from All of Statistics by Larry Wasserman, Exercises from Regression and Other Stories, and Baking Pi Tutorial for building a Baremetal OS on Raspberry Pi.

Will McGinnis is an entrepreneur and data science executive with over a decade of experience at the intersection of AI/ML, technology, and business strategy. He is currently the Chief Data Scientist at Scale Venture Partners, building AI infrastructure to augment investment decision-making. Will's background is in Mechanical Engineering, with a B.S. and M.S. from Auburn University. His research focused on multi-body dynamics and system wear. Previously, he was a co-founder of Predikto, which was later acquired by United Technologies (UTC). He then led Applied Data for Raytheon Technologies/UTC. Before joining Scale Venture Partners, Will led Data Science at Aumni, which was acquired by JPMorgan Chase & Co.

Stephen Turner is an Associate Professor at the University of Virginia School of Data Science and an advisor to several biotech startups. His research interests span genomics and biosecurity, AI safety in biotech, computational biology in synthetic biology and genome engineering, infectious disease modeling and forecasting in public health, and the polygenic architecture of human disease. Previously, he was a principal scientist at Colossal Biosciences, focusing on species restoration and conservation using synthetic biology and genome engineering. He also worked as a consultant and researcher building bioinformatics and data science solutions for the US Government in public health, defense, and national security. From 2011-2019, he was a professor in Public Health at the University of Virginia School of Medicine and founding director of UVA’s Bioinformatics Core. He taught graduate courses in data science and bioinformatics, and authored the Biological Data Science with R textbook.

Allan Butler is a data scientist based in Austin, Texas, who designs and deploys AI systems inside large organizations. He has spent over a decade working in statistics and machine learning, building production systems that span data engineering, model training, deployment pipelines, and governance. He earned his Master's from the University of Houston, focusing on demand forecasting and volatility modeling in secondary markets using ARCH/GARCH models. He works in AI and machine learning at H-E-B and brings his work to practitioner events and conferences.