Staff Data Scientist / Technical Lead (Full-Stack / Production ML)

About this role

About Appriss Retail

Appriss Retail is the total retail loss solution for omnichannel, unifying high-quality data across stores, online, and customer ser-vice to reduce returns, cut shrink, and manage incidents. Our products—Engage to reduce returns, Secure to cut shrink, and Incident to centralize visibility—help retailers move from reactive loss control to strategic profit protection. Together, they empower organizations to make better operations decisions, strengthen accountability, and put hundreds of millions back to the bottom line. Covering 40% of all U.S. transactions and active in 45 countries, Appriss Retail is trusted by 60+ of the top 100 U.S. retailers to deliver lasting performance improvement. Learn more at apprissretail.com.

Overview

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 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.

Essential Duties

Technical leadership & delivery

• 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 scienceprojects, taking into accountperformance,maintenance, and cost criteria.

• Set the standard for code quality: write production-grade Python and SQL, enforce reviewpractices, andmaintaindocumentation.

• Partner closely with engineering to integrate models and pipelines into core product infrastructure.

Strategy & stakeholders

• 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.

Required Qualifications

Experience

• 6+ years of experience in data science, data engineering, or a closely related technical discipline.

• 1+yearof formal technical lead experience over a team with the readiness and interest to grow into direct management.

Technical skills — required

• 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.

Required Education

• Master's degree or Bachelor's Degree in a technical, quantitative field

Preferred Qualifications

• Proficiencywith 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.

Interview Process / What to Expect

In addition to a technical deep-dive on your modeling skills, the interview process includes a system design conversation where you’ll walk through how you’d architect, deploy, and operate a model or pipeline in production — including infra and cost tradeoffs.

Benefits

At Appriss Retail, we offer a competitive and comprehensive benefits package designed to support your well-being at work and beyond. Benefits begin on your first day and include multiple medical plan options, dental and vision coverage, health savings and flexible spending accounts, paid parental leave, and supplemental coverage for life’s unexpected moments. We offer generous paid time off, a 401(k) with immediate vesting and company match, short- and long-term disability, and free access to health and wellbeing resources such as Calm and Sworkit. You’ll also have access to learning and development opportunities to help you grow your career. Our benefits support your well-being so you can perform your best in every part of life.

Reports to: Director of Data Science

Department: Data…