Senior Data Scientist

What you'll do

• Own end-to-end delivery of data science projects, from problem scoping through production deployment
• Define and ship modeling methodology that powers Samba's data products, including model selection, evaluation frameworks, and reproducibility standards
• Apply core ML and statistics (regression, classification, clustering, model evaluation, experimental design, causal inference) to billion-row, real-world data
• Build production-quality Python and PySpark on Databricks: well-tested, documented, reusable
• Partner with Data Engineering to define data requirements, validate pipelines, and ensure model inputs are reliable and production-ready
• Build and operate advanced AI systems using modern methodologies: retrieval-augmented generation (RAG), LLM-augmented modeling and Graph Neural Networks
• Design AI-driven modeling approaches that improve as signals evolve, supporting agentic decision-making at platform scale
• Integrate LLMs and agentic workflows into production ML pipelines where they extend modeling capability and unlock new product surfaces
• Drive technical design for modeling components within your scope, producing clear solution documents covering problem statement, approach, metrics, and trade-offs
• Translate business requirements into modeling solutions in close collaboration with product, engineering, and go-to-market partners
• Uphold high standards for production-quality data science
• Mentor data scientists on the team through structured feedback, pairing, and design review
• Establish and operate MLOps practices: experiment tracking, pipeline orchestration (Airflow), model monitoring, retraining workflows, and reproducibility standards
• Apply privacy-compliant data handling practices, including GDPR, CCPA, and Samba's data governance policies

Requirements

• 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
• Clear communicator who translates technical work into design docs, user stories, and cross-functional conversations
• Active mentor who invests in others, gives direct feedback, and raises the bar for the team

Tech stack

Benefits