8+ years of hands-on data science experience with a Bachelor's degree in Statistics, Data Science, Computer Science, Mathematics, or a related quantitative field (or 6+ years with a Master's, 3+ years with a PhD, or equivalent).
Demonstrated ability to own and deliver complex, multi-sprint data science projects from problem scoping through production deployment.
Solid command of core ML and statistics, including neural networks, regression, classification, clustering, model evaluation, experimental design, and causal inference, applied to billion-row datasets.
Track record of building methodology, not just applying it: data analysis, model selection, evaluation frameworks, and solid documentation of decision processes
Production experience with vector databases (Pinecone, Weaviate, Milvus, pgvector, or equivalent) for retrieval, matching, or inference at scale.
Advanced Python with production-quality, tested code; strong SQL and PySpark on billion-row datasets.
Databricks, Delta Lake, and job orchestration (Airflow); hands-on production experience on AWS, GCP, and Databricks.
MLOps proficiency: experiment tracking, pipeline orchestration, model monitoring, reproducible deployment.
Experience designing and operating agentic AI systems in production: prompt engineering, agent orchestration, tool use, or integration of LLMs into ML pipelines.
A clear communicator who translates technical work into design docs, user stories, and cross-functional conversations.
An active mentor who invests in others, gives direct feedback, and raises the bar for the team as a whole.
