Data Science Manager (Full-Stack / Production ML)
About The Position
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 real data platforms and AI/ML systems using a modern stack, has meaningful experience with LLMs and agentic architectures, and can operate credibly 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.
Requirements
- 6+ years of experience in data science, data engineering, or a closely related technical discipline.
- 1+ year of 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, CloudFormation, or equivalent) and containerization (Docker, and ideally Kubernetes or a managed container service).
- Has made and defended a build-vs-buy or cost/latency tradeoff on a production ML or data system.
- Familiarity with ML platform tooling: MLflow, feature stores, model registries, or similar.
Nice To Haves
- Proficiency with modern data stack tooling: dbt, Airflow, Spark, or equivalent.
- Demonstrated LLM experience: prompt engineering, RAG, fine-tuning, or agent frameworks (LangChain, LlamaIndex, or equivalent)
- Experience and familiarity with agentic AI architectures: multi-step reasoning, tool use, memory, and orchestration.
- Experience in retail, fraud detection, or transaction-level data at scale.
Responsibilities
- Own end-to-end delivery of high-impact data science projects — from ambiguous business request to production-ready system.
- Design and maintain data 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.
- Guide cloud infrastructure architecture for data 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, and maintain documentation.
- 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.
Benefits
- Multiple medical plan options
- Dental and vision coverage
- Health savings and flexible spending accounts
- Paid parental leave
- Supplemental coverage for life’s unexpected moments
- Generous paid time off
- 401(k) with immediate vesting and company match
- Short- and long-term disability
- Free access to health and wellbeing resources such as Calm and Sworkit
- Learning and development opportunities
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What This Job Offers
Job Type
Full-time
Career Level
Manager
