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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonSQLdbtdata transformationAI toolsLLM APIsprompt engineeringMLOpspipeline orchestrationdata visualization

Soft Skills

cross-functional deliverystakeholder alignmentproblem-solvingcommunicationcollaborationanalytical thinkingproject managementadaptabilityattention to detailbest practices establishment