Senior Data Science Engineer, Data Science & Analytics (DSA)

country: US all locations: [San Francisco Bay Area] commitment: Full-time department: Data Science, ML/CV location: San Francisco Bay Area team: Data Science Key Responsibilities: Own production pipelines end-to-end — design, build, and maintain robust data science pipelines that run reliably in production, including monitoring, alerting, and iterative improvement Scope and deliver features — take ambiguous problems, define clear analytical approaches, and ship client-facing solutions in collaboration with Engineering and Product Management Drive cross-functional delivery — proactively identify blockers, align stakeholders across teams, and move projects forward with minimal oversight Apply AI tooling to accelerate work — leverage LLMs, agents, and other AI-assisted workflows to increase the speed and quality of analysis and development Translate retail data into decisions — connect store-level signals (inventory, on-shelf availability, task execution, etc.) to meaningful business outcomes for both internal teams and retail clients Raise analytical standards — establish best practices for reproducibility, documentation, and code quality across the team's DS work Build conversational data experiences — design and prototype AI agent or chatbot interfaces that allow internal or external users to query and explore retail data through natural language (nice to have) Qualifications: Required 5+ years of experience in data science or a closely related role, with demonstrable delivery of production features (not just research or prototyping) Strong Python skills; comfortable writing production-quality, version-controlled code Solid SQL and experience working with large-scale cloud data platforms (GCP/BigQuery preferred) Experience with dbt for data transformation — writing models, tests, and documentation as part of a production analytics engineering workflow Experience owning the full lifecycle of a data science feature: scoping, building, shipping, and maintaining Proven ability to work across functions — you've partnered with Engineering, Product, or Commercial teams and know how to communicate tradeoffs and drive alignment Retail industry experience strongly preferred (store operations, inventory, merchandising, supply chain, or equivalent) Hands-on experience using AI tools (LLM APIs, coding assistants, prompt engineering) to accelerate analytical work Preferred Familiarity with MLOps practices, pipeline orchestration (Airflow or similar), model monitoring, CI/CD for data science workflows Experience with data visualization tools (Looker, Tableau, or similar) for communicating findings to non-technical stakeholders Background in experimentation design (A/B testing, causal inference) Why You'll Love Working with Us: Ownership that matters — you'll have real scope over systems and features that run in production and directly affect how our retail partners operate Cutting-edge stack — GCP, BigQuery, Airflow, and an evolving AI toolchain with a strong appetite for experimentation High-signal environment — focused team where your work is visible and your technical judgment is trusted Retail at scale — Simbe's data spans thousands of stores and billions of shelf observations, a genuinely rich and challenging domain