Data Science Manager

Appriss Retail

63d•$160,000 - $170,000•Remote

About The Position

The Data Science Manager is a player-coach who leads a small, high-output 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. 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.

Requirements

Master's degree in a quantitative field, or bachelor's with significant professional experience.
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.
Hands-on ML experience: model training, evaluation, deployment, monitoring, and iteration.
Strong software engineering practices: version control, code review, testing, and CI/CD familiarity.
Ability to scope and deliver complex analytical projects independently from vague inputs.
Cloud data platform experience: Snowflake, Azure (preferred), AWS, or GCP.

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.
Familiarity with ML platform tooling: MLflow, feature stores, model registries, or similar.

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.