Senior Data Scientist

What You'll Do

Modeling and ML Development
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 solid command of 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.
AI and Agentic Capabilities
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.

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Integrate LLMs and agentic workflows into production ML pipelines where they extend modeling capability and unlock new product surfaces.

Technical Contribution and Collaboration

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.

MLOps and Production Practice

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.

Who You Are

Required

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.

Preferred

Knowledge graph design (RDF, OWL, SPARQL, or equivalent graph frameworks), Natural Language Processing, Background in ad tech, CTV/OTT, ACR, audience activation, identity resolution, or measurement methodologies.

Experience with causal inference (A/B testing, synthetic control, uplift modeling).