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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Data Scientist / Technical Lead (Full-Stack / Production ML) - **Company:** Appriss Retail - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $24,960 - $31,200 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Software Quality, Code Review, Continuous Integration, Information Engineering, Data Infrastructure, Data Systems, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Performance Tuning, Azure Machine Learning, Software Engineering, SQL Databases, Feature Store, Cloud Platform System, Retrieval-Augmented Generation, Large Language Models, Snowflake, Prompt Engineering, Apache Spark, Llamaindex, Agentic-AI, Cloudformation, Containerization, Kubernetes, Production Code, Data Management, Machine Learning Operations, Terraform, Software Version Control, Docker - **Published:** October 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/staff-data-scientist-technical-lead-full-stack-production-ml--171b3a0e-fadf-4be1-b033-02f6247abb05 ## About the Role Data Science, Technical Leadership, Retail, Coaching, Artificial Intelligence (AI), Data Science, Technical Leadership, Data Modeling, Technical Delivery, Production Systems, * 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. 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 * 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. ## Description 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. Essential Duties Technical leadership & delivery * 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. 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., Supervisory Duties: Not immediately, though sets technical direction and mentors the team (who report to the Director), with the opportunity to grow into direct management over time.