Data Scientist developing analytical solutions for customer data transformation and supporting critical business decisions. Focus on quality and integrity of data through statistical models and analysis.
About the role
- Data Scientist owning and delivering production-grade data pipelines at Simbe Robotics. Collaborating with Product Management, Engineering, and Commercial teams to surface insights from retail data.
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)*
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
- Familiarity with MLOps practices, pipeline orchestration (Airflow or similar), model monitoring, CI/CD for data science workflows (nice to have)
- Experience with data visualization tools (Looker, Tableau, or similar) for communicating findings to non-technical stakeholders (nice to have)
- Background in experimentation design (A/B testing, causal inference) (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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