About the Role
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.
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
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
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
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