Senior Manager, AI Policy and Channel

Bristol Myers Squibb•Princeton, NJ

About The Position

The Senior Manager, Artificial Intelligence, Policy and Channel (AIPC) is a hands-on technical leader who develops, deploys, and maintains data science and machine learning pipelines, solutions, and products that drive commercialization excellence at BMS. This role provides AI-driven insights that help BMS anticipate and respond to channel-level business trends, payer contract dynamics, legislative policy shifts, and new indication launches. The Senior Manager bridges technical expertise with business acumen, partnering closely with cross-functional stakeholders to deliver scalable, impactful solutions that improve forecast accuracy, net revenue visibility, and contracting strategies across BMS's portfolio.

Requirements

Education Required: Bachelor's degree (BA/BS) in a quantitative field such as Data Science, Computer Science, Mathematics, Statistics, Engineering, or related discipline.
Experience Minimum 3 years of hands-on experience developing and deploying machine learning pipelines to solve real-world business problems.
Proven track record of delivering measurable business impact through AI and advanced analytics solutions.
Experience managing AI product lifecycles and integrating solutions into operational business processes.
Technical Skills Statistical Methods: Strong foundation in statistical methods and their application to business problems, preferably in biopharmaceutical commercialization or healthcare settings.
Machine Learning Expertise: Experience designing and implementing supervised, unsupervised, and reinforcement learning methods using common ML toolkits (TensorFlow, PyTorch, scikit-learn).
Forecasting: Hands-on experience with time series forecasting and Monte Carlo simulations.
Programming: Proficiency in Python is required; experience with R or other statistical programming languages is a plus.
MLOps and Cloud: Familiarity with CI/CD workflows, Git/GitHub version control, MLOps workflows, cloud-based environments (AWS, Azure), and workflow orchestration tools (Airflow, Dagster).
Soft Skills and Competencies Communication Excellence: Outstanding communication and presentation skills, with proven ability to translate technical complexity into clear value statements and actionable insights for diverse audiences.
Stakeholder Management: Strong interpersonal skills to drive stakeholder engagement, build trust, and foster cross-functional collaboration.
Problem-Solving: Resourceful and proactive approach to overcoming data or modeling challenges, with flexibility to adjust approaches as new information emerges.
Agility: Ability to thrive in a dynamic environment with evolving market conditions, policy changes, and business priorities.
Ownership and Accountability: Demonstrated ability to take ownership of projects and drive them to successful completion.

Nice To Haves

Preferred: Advanced degree (MS or Ph.D.) in Computer Science, Computational Biology, Bioinformatics, Data Science, or related field.
Advanced AI (Preferred): Experience with Natural Language Processing (NLP) techniques, Large Language Models (training/fine-tuning), and agentic AI frameworks.
Knowledge of biopharmaceutical market access, pricing, value demonstration, and reimbursement landscape.
Understanding of channel dynamics, payer contracting, and healthcare policy implications for pharmaceutical commercialization.
Experience working in matrixed organizations and influencing without direct authority.

Responsibilities

Hands-On Technical Delivery: Machine Learning Pipeline Development
Design and Deploy ML Pipelines: Lead the hands-on design, development, and deployment of end-to-end machine learning pipelines that address critical business challenges in Market Access, Pricing, and Value Demonstration.
Automate Data Workflows: Build and maintain automated workflows to process and analyze complex datasets, including biopharmaceutical rebates, contracts, government policies, and channel dynamics.
Model Development and Optimization: Apply advanced machine learning techniques (time series forecasting, Monte Carlo simulations, supervised/unsupervised learning) to create robust, scalable models that support real-time decision-making.
Ensure Solution Reliability: Maintain high standards for reliability, scalability, and maintainability of deployed solutions, implementing monitoring and version control best practices.
Technical Innovation: Stay current with emerging ML/AI technologies (NLP, Large Language Models, agentic AI frameworks) and evaluate their applicability to business problems.
Stakeholder Engagement and Cross-Functional Collaboration
Partner Across Functions: Collaborate closely with Business Insights & Technology, Commercialization, Finance, Brand teams, and Market Access groups to co-develop AI-driven solutions aligned with strategic business priorities.
Requirements Gathering: Engage with brand finance directors, pricing and access leads, and forecasting teams to gather requirements, understand business needs, and ensure AI solutions meet user expectations.
Translate Technical to Business: Act as a liaison between technical teams and non-technical stakeholders, translating complex model results and technical insights into clear, actionable business strategies.
Build Trust and Adoption: Foster a collaborative environment where stakeholders feel ownership of AI outcomes, ensuring solutions are integrated into core workflows and trusted for decision-making.
Communication Excellence: Deliver presentations and updates to diverse audiences, clearly explaining model performance, variances, and business implications.
AI Product Lifecycle Management
Project Planning and Execution: Develop and execute detailed project plans that align with business timelines, ensuring on-time delivery of high-quality AI solutions against key milestones.
Integration and Rollout: Ensure timely integration of AI solutions into business platforms (e.g., Anaplan forecasting platform) and manage rollout to new brands, channels, and product archetypes.
Impact Analysis: Conduct post-deployment analysis to measure solution impact, validate business value, and identify opportunities for continuous improvement.
Iterative Enhancement: Establish feedback loops with stakeholders to refine models, incorporate new data sources, and adapt to evolving market conditions and policy changes.

Benefits

Health Coverage: Medical, pharmacy, dental, and vision care.
Wellbeing Support: Programs such as BMS Well-Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP).
Financial Well-being and Protection: 401(k) plan, short- and long-term disability, life insurance, accident insurance, supplemental health insurance, business travel protection, personal liability protection, identity theft benefit, legal support, and survivor support.
Work-life benefits include: Paid Time Off US Exempt Employees: flexible time off (unlimited, with manager approval, 11 paid national holidays (not applicable to employees in Phoenix, AZ, Puerto Rico or Rayzebio employees) Phoenix, AZ, Puerto Rico and Rayzebio Exempt, Non-Exempt, Hourly Employees: 160 hours annual paid vacation for new hires with manager approval, 11 national holidays, and 3 optional holidays Based on eligibility, additional time off for employees may include unlimited paid sick time, up to 2 paid volunteer days per year, summer hours flexibility, leaves of absence for medical, personal, parental, caregiver, bereavement, and military needs and an annual Global Shutdown between Christmas and New Years Day. All global employees full and part-time who are actively employed at and paid directly by BMS at the end of the calendar year are eligible to take advantage of the Global Shutdown.

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