A hybrid Data & ML role at Samba.
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Modeling and ML Development
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Own end-to-end delivery of data science projects, from problem scoping through production deployment.
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Define and ship modeling methodology that powers Samba's data products, including model selection, evaluation frameworks, and reproducibility standards.
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Apply solid command of core ML and statistics (regression, classification, clustering, model evaluation, experimental design, causal inference) to billion-row, real-world data.
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Build production-quality Python and PySpark on Databricks: well-tested, documented, reusable.
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Partner with Data Engineering to define data requirements, validate pipelines, and ensure model inputs are reliable and production-ready.
AI and Agentic Capabilities
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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
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Drive technical design for modeling components within your scope, producing clear solution documents covering problem statement, approach, metrics, and trade-offs.
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Translate business requirements into modeling solutions in close collaboration with product, engineering, and go-to-market partners.
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Uphold high standards for production-quality data science.
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Mentor data scientists on the team through structured feedback, pairing, and design review.
MLOps and Production Practice
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Establish and operate MLOps practices: experiment tracking, pipeline orchestration (Airflow), model monitoring, retraining workflows, and reproducibility standards.
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Apply privacy-compliant data handling practices, including GDPR, CCPA, and Samba's data governance policies.
Required
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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).
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Demonstrated ability to own and deliver complex, multi-sprint data science projects from problem scoping through production deployment.
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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.
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Track record of building methodology, not just applying it: data analysis, model selection, evaluation frameworks, and solid documentation of decision processes
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Production experience with vector databases (Pinecone, Weaviate, Milvus, pgvector, or equivalent) for retrieval, matching, or inference at scale.
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Advanced Python with production-quality, tested code; strong SQL and PySpark on billion-row datasets.
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Databricks, Delta Lake, and job orchestration (Airflow); hands-on production experience on AWS, GCP, and Databricks.
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MLOps proficiency: experiment tracking, pipeline orchestration, model monitoring, reproducible deployment.
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Experience designing and operating agentic AI systems in production: prompt engineering, agent orchestration, tool use, or integration of LLMs into ML pipelines.
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A clear communicator who translates technical work into design docs, user stories, and cross-functional conversations.
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An active mentor who invests in others, gives direct feedback, and raises the bar for the team as a whole.
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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.
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Experience with causal inference (A/B testing, synthetic control, uplift modeling).
Samba
Data & ML
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