Data Scientist (GenAI/LLM Applications & Supply Chain Automation)

Quick Facts

• Role: Data Scientist focused on GenAI/LLM application development and supply chain business process re-engineering

Description

Design and architect end-to-end GenAI/LLM-powered applications and agent workflows to solve complex supply chain business problems. Collaborate with business teams to translate opportunities into technical solutions, and with data engineers to integrate LLM APIs/frameworks and operationalize robust, production-quality systems. Build RAG pipelines and vector search capabilities with interactive visualizations, define KPIs for AI value, and present results to leadership in non-technical terms.

Responsibilities

• Engage business teams to identify opportunities and translate requirements into solutions

• Collaborate with engineers/infrastructure partners to integrate LLM APIs and frameworks and deploy reliable services

• Build and improve production-quality solutions with engineering, evals, monitoring, feedback loops, and iteration

• Research and evaluate emerging GenAI/agent frameworks and visualization tools

• Create RAG pipelines, vector search, and self-service dashboards to deliver actionable insights at scale

• Wrangle/analyze data, perform feature engineering, and define metrics to quantify business value

• Translate complex AI/ML concepts into business narratives for senior leadership

Requirements

• PhD (CS, software engineering, statistics, applied math, data science, operations research, or related) + 5+ years experience OR MS (related) + 8+ years hands-on industry experience

• Strong software engineering fundamentals with production-quality Python and TypeScript/JavaScript and advanced SQL

• Production-quality coding practices: Git, code review, testing, documentation

• Experience building end-to-end applications (backend services + RESTful APIs such as FastAPI; front-ends such as React/Streamlit)

• Experience deploying GenAI/LLM and ML models on cloud with MLOps, CI/CD, model versioning, containerization/orchestration, and monitoring

• Hands-on LLM API integration (e.g., OpenAI/Anthropic) and agent frameworks (LangChain/LangGraph) using MCP servers, agents, and skills

• Expertise in designing evals to measure LLM quality, accuracy, and reliability

• Background in AIML libraries (Scikit-learn, TensorFlow, PyTorch) and predictive modeling

• Experience with forecasting/optimization/simulation in supply chain or operations

• Ability to own projects end-to-end from scoping/prototyping through deployment and iteration