Analytical AI Engineer - II

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 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 Have

Exposure 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 Qualifications

Bachelor'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 pa