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

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 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.

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.

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.

People & team

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.

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

Education & Experience

Master's degree in a quantitative field, orbachelor'swith significant professional experience.

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.

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.

Hands-on ML experience: model training, evaluation, deployment, monitoring, and iteration.

Strong software engineering practices: version control, code review, testing, and CI/CD familiarity.<

Data Science Manager at Appriss Retail