Production ML)

Appriss Retail

6h•$160,000 - $170,000•Remote

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 Staff Data Scientist / Technical Lead (Full-Stack / Production ML), you would set technical direction for the team and have the opportunity to grow into direct management over time. 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 formal technical lead experience over a team with the readiness and interest to grow into direct management.
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.
Master's degree or Bachelor's Degree in a technical, quantitative field

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.
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.