About The Position

We are looking for an experienced Data Scientist to join the Data Science & Analytics team, owning production-grade data pipelines from ideation through delivery. This is an engineering-forward DS role — you'll partner closely with Product Management, Engineering, and Commercial teams to ship reliable, user-facing features that surface insights from our retail data at scale. You are someone who thrives at the intersection of data science and software engineering: you write production code, own the reliability of the systems you build, and drive cross-functional projects to completion without waiting to be unblocked. Leveling (Senior or Staff) and compensation will be determined through the interview process based on your background and technical depth.

Requirements

  • 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

Nice To Haves

  • 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)

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)

Benefits

  • 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
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