Data Science Manager; Full-Stack​/Production ML

We are looking for a player-coach who leads a small, high-output data science team while staying deeply hands‑on. This role owns the full scope of data science at Appriss Retail — data engineering, governance, and production model delivery — not just model building. The right candidate has built and shipped realdataplatforms andAI/ML systems using a modern stack, has meaningful experience with LLMs and agentic architectures, and canoperatecredibly in both the technical weeds and the business conversation.

As a Data Science Manager with 2–4 direct reports from day one, you would set technical direction for the team. This is not a role for someone who manages from a distance. You will write code, review pipelines, define data contracts, and drive architectural decisions — while also growing and directing the team around you.

This is a full-stack role, not a model‑building role with a handoff. If your last few projects ended when you handed a notebook or a trained model to a data engineering or ML engineering team to put into production, this probably isn’t the right fit.

• Own end-to-end delivery of high-impact data science projects — from ambiguous businessrequestto production-readysystem.
• Design andmaintaindata pipelines, data models, and governance standards alongside your team; treat infrastructure as a first-class product concern.
• Build, evaluate, and iterate on ML models in production; lead experimentation rigor, monitoring, and lifecycle management.
• Architect and ship LLM-integrated features and agentic workflows — including prompt engineering,tool use, and output evaluation.
• Guidecloud infrastructurearchitecturefordata science projects, taking into account performance,maintenance, and cost criteria.
• Set the standard for code quality: write production-grade Python and SQL, enforce review practices, andmaintaindocumentation.
• Partner closely with engineering to integrate models and pipelines into core product infrastructure.

• Directly manage 2–4 data scientists; provide technical mentorship, career development, and clear performance expectations.
• Define team operating norms: sprint planning, code review, documentation, and delivery accountability.
• Recruit and grow the team as the function scales.

• Translate ambiguous business problems into well-scoped analytical and modeling work with defined success criteria.
• Partner with product, engineering, and business stakeholders to ensure data work is grounded in real source systems and product context — not isolated analysis.
• Contribute to the data and analytics roadmap, balancing near-term delivery with longer-term platform investment.
• Communicate clearly to non-technical audiences; influence decisions with data and model outputs.

• 6+ years of experience in data science, data engineering, or a closely related technical discipline.
• 1+yearof direct people management or formal technical lead experience over a team.

• Expert-level SQL and Python; production code, not just analysis scripts.
• Deep understanding of data infrastructure: pipelines, warehousing, data modeling, and source system behavior.
• Strong software engineering practices: version control, code review, testing, and has built or maintained a CI/CD pipeline for a data or ML workload..
• Ability to scope and deliver complex analytical projects independently from vague inputs.
• Cloud data platform experience:
  Snowflake, Azure (preferred), AWS, or GCP.
• Working knowledge of infrastructure-as-code (Terraform, Cloud Formation, or equivalent) and containerization (Docker, and ideally Kubernetes or a managed container service).
• Has made and defended a build-vs-buy or…