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

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.

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.

• 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.

• 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.

• 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

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.

We are proud to be an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to protected characteristics.