Data Science Manager – Full-Stack, Production ML

🕒 August 12

🇺🇸 United States – Remote

💵 $160k - $170k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

👻 Ghost score 14%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Appriss Retail

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Appriss Retail is a company that specializes in providing solutions to prevent fraud and shrink in the retail industry. For over 25 years, they have been helping retailers protect their profits against fraudulent and abusive returns, claims, and appeasements. Their solutions, such as Appriss Secure, Appriss Engage, and Appriss Incident, use AI-based analytics and real-time transaction analysis to identify and resolve instances of shrink or margin erosion. Appriss Retail is trusted by over 60 of the top 100 U. S. retailers.

📋 Description

• 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 • 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, considering performance, maintenance, and cost • Write production-grade Python and SQL; enforce code review practices and maintain documentation • Partner with engineering to integrate models and pipelines into core product infrastructure • Directly manage 2–4 data scientists • Provide technical mentorship, career development, and performance expectations • Define team operating norms including sprint planning, code review, documentation, and delivery accountability • Recruit and grow the team as the function scales • Translate ambiguous business problems into scoped analytical and modeling work with defined success criteria • Partner with product, engineering, and business stakeholders • Contribute to the data and analytics roadmap • Communicate data and model outputs to non-technical audiences • Report to the Director of Data Science • Travel up to 10% for training and conferences

🎯 Requirements

• 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 • Strong software engineering practices: version control, code review, testing, and CI/CD pipeline experience for a data or ML workload • Ability to scope and deliver complex analytical projects independently from vague inputs • Cloud data platform experience with Snowflake, Azure, AWS, or GCP • Working knowledge of infrastructure-as-code such as Terraform or CloudFormation • Containerization experience with Docker, ideally Kubernetes or a managed container service • Production ML or data system build-vs-buy or cost/latency tradeoff experience • Familiarity with ML platform tooling such as MLflow, feature stores, or model registries • Master's degree or Bachelor's Degree in a technical, quantitative field • Preferred: proficiency with dbt, Airflow, Spark, or equivalent • Preferred: demonstrated LLM experience including prompt engineering, RAG, fine-tuning, or agent frameworks • Preferred: familiarity with agentic AI architectures including multi-step reasoning, tool use, memory, and orchestration • Preferred: experience in retail, fraud detection, or transaction-level data at scale • Willingness to travel up to 10% for training and conferences

🏖️ Benefits

• Multiple medical plan options • Dental and vision coverage • Health savings and flexible spending accounts • Paid parental leave • Supplemental coverage for life’s unexpected moments • Generous paid time off • 401(k) with immediate vesting and company match • Short- and long-term disability • Free access to health and wellbeing resources such as Calm and Sworkit • Learning and development opportunities • Eligible for a 12–15% bonus in addition to the base salary • Remote work