Analytical AI Engineer - II

Job description

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 OverviewThe 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 ResponsibilitiesBuild 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 rather than disconnected notebooks Practical use of PySpark to process and transform large, distributed datasets efficiently, including partitioning and join strategies that keep feature-engineering jobs performant at scale GenAI & LLM Applications Applied exposure to Generative AI and LLM-based techniques (prompting, summarization, retrieval-style workflows) to automate insight generation or summarize analytical findings for stakeholders, ideally with some familiarity with Azure OpenAI, AWS Bedrock, or Google Vertex AI Tools, Engineering Practices & Collaboration Disciplined use of MLflow (or equivalent) to log experiments, compare runs, and version models so results are reproducible and auditable by teammates Strong day-to-day Python and SQL coding habits — modular, tested, documented — suited to code that will run in production pipelines, not just exploratory notebooks Regular use of VS Code (or similar) paired with AI coding tools such as GitHub Copilot or Claude Code to move faster without sacrificing code review quality Solid Git/GitHub workflow habits (branching, PRs, code review) and comfort contributing to CI/CD pipelines that deploy models and data jobs Experience working within Agile/Scrum ceremonies (sprint planning, standups, retros) as part of a cross-functional analytics team Good to HaveExposure to GenAI & Agentic Frameworks like LangChain, LangGraph, Pydantic AI, LlamaIndex, etc. Familiarity with LLM platforms like Azure OpenAI, AWS Bedrock, Google Vertex AI, etc. Experience with knowledge graph or ontology tools/technologies (e.g., RDF, OWL, SPARQL, Neo4j, or similar) Experience with BI/visualization tools (e.g., Tableau, Power BI, Looker) Exposure to cloud platforms (AWS, Azure, or GCP) for analytics or model deployment Required QualificationsBachelor's Degree in Computer Science, Statistics, Mathematics, Data Science, or a related field, with 3+ years of progressively responsible experience in analytics, data science, or applied AI/ML roles Solid grounding in statistics and probability, with working knowledge of experimental design Experience with Databricks for data pipeline development and analytical/ML workloads Working knowledge of PySpark for distributed data processing Familiarity with MLflow (or equivalent) for experiment tracking Experience building analytical dashboards; exposure to semantic layer concepts Experience or hands-on exposure to building semantic layers, ontologies, or knowledge graphs, and structuring them for consumption by AI Agent or GenAI-based applications Experience with core ML libraries and frameworks including Scikit-learn, NumPy, Pandas, PyTorch, TensorFlow/Keras, etc. Proficiency in SQL and experience working with structured and semi-structured datasets Strong Python programming skills for analytical modeling and pipeline development Proficiency with Git for version control We hire for skills and capabilities, not just credentials – if this role excites you, but doesn’t perfectly match your resume, we encourage you to apply anyway. How We Work Where you work matters – because collaboration, innovation and patient impact happen in many settings. Our roles are structured across four work models: site-essential, site-by-design, field-based and remote-by-design. The model assigned to this role is based on its core responsibilities. Learn more at https://careers.bms.com/ways-of-working. Supporting People with Disabilities BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles. Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer. If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to adastaffingsupport@bms.com. Visit careers.bms.com/eeo-accessibility to access our complete Equal Employment Opportunity stat