At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it.
Role Overview
The Analytical AI Engineer - II builds and maintains analytical and machine learning models, dashboards, and semantic layers/knowledge graphs that turn business questions into data-driven insights, working under the guidance of senior team members. This role also applies Generative AI and LLM-based techniques to make analytics outputs and semantic assets consumable by AI Agent solutions and downstream applications.
Key Responsibilities
Build and maintain analytical and machine learning models for forecasting, classification, regression, anomaly detection, and other data-driven decision-making use cases, under the guidance of senior team members
Collaborate with business stakeholders to understand requirements, translate business questions into analytical tasks, and apply statistical methods to generate insights
Build and maintain analytical dashboards, and contribute to semantic layer development to support consistent, reusable metrics and self-service reporting
Contribute to the design and development of semantic layers, ontologies, and knowledge graphs, and help make them accessible and consumable for AI Agent solutions and downstream applications
Build semantic and context layers (business definitions, metrics, entity relationships, metadata, and curated retrieval context), and consume them through Agentic AI and Generative AI solutions so AI agents and LLM applications can deliver accurate, grounded, and explainable insights
Build and maintain analytics and ML pipelines — from data extraction, cleaning, and feature engineering through model development, validation, and deployment
Develop and support data pipelines for analytical modeling and reporting needs
Apply Generative AI and LLM-based techniques where relevant to support analytics workflows
Track experiments and manage model versions using experiment-tracking tools
Write clean, well-documented, production-ready code for analytical modeling and pipeline development
Use AI coding assistants to accelerate development productivity
Manage source code and version control, and contribute to CI/CD workflows
Collaborate with team members following Agile/Scrum methodologies
Desired Experience
Analytics & Machine Learning
Hands-on experience building forecasting , classification , regression , and anomaly detection models with libraries such as Scikit-learn , PyTorch , and TensorFlow/Keras , iterating under senior guidance from problem framing through model evaluation
Comfort partnering directly with business stakeholders to turn open-ended questions into testable hypotheses, then running A/B tests and other hypothesis-testing designs in Python / SQL to quantify impact and communicate findings back in business terms
Dashboards & Semantic Layer
Experience building and maintaining production dashboards (e.g. in Tableau , Power BI , or Looker ) backed by a shared semantic layer, so metrics stay consistent across self-service reports rather than being redefined per dashboard
Exposure to modeling semantic layers , ontologies , and knowledge graphs (e.g. with RDF , OWL , SPARQL , or Neo4j ) and structuring them so an AI Agent or downstream GenAI application can query and reason over them, not just a human reading a report
Data & ML Pipelines
Working experience standing up end-to-end pipelines on Databricks — extraction, cleaning, feature engineering, model training/validation, and deployment — as a single reproducible workflow…