Carl Anderson is a senior data science leader with 7+ years of experience building and scaling teams and ML/AI-driven products that serve millions of users and drive measurable business impact. He has led data functions at consumer and sustainability-focused companies—deploying dozens of production models from personalization and recommenders to transactional intelligence and carbon-monitoring MRV systems. A published author of Creating a Data Driven Organization, Carl blends hands-on MLOps, analytics engineering (dbt), and data platform strategy with executive-level technology leadership and CxO collaboration. His career highlights include multi-million dollar savings from membership-model redesigns, dramatic engagement lifts from recommender systems, and launching a global food data capability at WW. He also contributes to open-source work—adding and maintaining real-world web scrapers in the popular recipe-scrapers repo—showing ongoing practical coding chops. Based in Jersey City, he pairs a PhD-trained analytical mind with pragmatism for shipping robust, production-ready data products.
7 years of coding experience
14 years of employment as a software developer
BSc biological sciences, BSc biological sciences at Durham University
Doctor of Philosophy (PhD) Mathematical Biology, Doctor of Philosophy (PhD) Mathematical Biology at The University of Sheffield
Master of Science (MSc) Biological Computation, Master of Science (MSc) Biological Computation at University of York
Contributions:4 reviews, 12 commits, 11 PRs in 10 days
Contributions summary:Carl primarily contributes to integrating new recipe websites into the `recipe-scrapers` project. They add new scrapers, implement the necessary code to extract recipe data, and write corresponding tests. Furthermore, they modify existing scrapers to adapt to changes in website CSS or DOM structure, ensuring the continued functionality of the data extraction process. Their work also includes general code maintenance tasks such as code formatting and removing duplicate imports, suggesting a good understanding of the project's codebase.
Tools to handle best practices for LookML dev. Contains three tools: LookML updater, linter, and grapher
Contributions:2 releases, 7 commits, 11 PRs in 7 months
linterdevlookmlupdatergrapher
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