Associate Director, Commercial AI and Advanced Analytics- Immunology

Summary

The Associate Director, Commercial AI and Advanced Analytics is responsible for delivering advanced commercial measurement and decision intelligence across BMS brands and therapeutic areas.

Responsibilities

• Deliver Advanced Marketing & Commercial Measurement
• Own end-to-end development and execution of advanced Marketing Mix (MMx) and Key Driver Analyses (KDA) to support brand planning, budget optimization, and performance assessment.
• Apply advanced modeling techniques to quantify incremental impact across channels (sales force, digital, TV, congresses, patient programs).
• Translate modeling outputs into clear business implications for brand strategy, media mix, and investment allocation.
• Support annual brand planning, in-year optimization, and scenario analysis using MMx outputs.
• Build & Operate Advanced MMx Capabilities
• Design and run advanced MMx models using Bayesian, Huber approaches, including: Adstock and carryover modeling, Saturation and diminishing returns curves, Distributed lag and non-linear response functions, Cross-channel interaction effects.
• Develop scalable and repeatable MMx pipelines aligned with enterprise standards and automated platforms.
• Partner with centralized teams to integrate MMx into self-service and agent-enabled analytics solutions.
• Ensure models are explainable, auditable, and fit for use in a regulated pharmaceutical environment.
• Agentic & Always-On Insights Delivery
• Contribute to the design and execution of agentic and always-on insights capabilities that provide continuous decision support to Brand, Omnichannel, and Field teams.
• Operationalize MMx, KDA, and causal models into always-on analytics pipelines with defined refresh cadence, data quality checks, and monitoring.
• Structure model outputs (drivers, sensitivities, scenarios, constraints) to be consumable by AI-enabled decision tools and agent-based workflows.
• Support "what changed / why" and forward-looking recommendation logic using causal and driver-based methods.
• Ensure transparency, lineage, and explainability from data → model → insight → recommendation, with appropriate human-in-the-loop review.
• Define and maintain the Key Business Questions (KBQ) framework and business context layer, bridge business intent with the underlying technical semantic layer.
• Partner with centralized platform, engineering, and Hyderabad teams to define inputs, features, and guardrails for agent-enabled insights.
• Design insights to be role-based, actionable, and embedded into brand planning, optimization, and omnichannel execution workflows.
• Drive adoption of agentic AI tools through role-based training, and structured feedback; serve as the primary business point of contact and channel user feedback into roadmap iteration.
• Governance, Compliance & Quality
• Adhere to data privacy, AI governance, and model validation standards (HIPAA, GDPR/CCPA, internal AI controls).
• Maintain clear documentation of assumptions, methods, and limitations.
• Participate in model reviews and audits with Legal, Privacy, and Governance stakeholders.
• Stakeholder Partnership
• Act as a trusted analytics partner to Brand, Marketing, Omnichannel, Field, and Finance teams.
• Present insights in concise, business-first narratives suitable for brand leadership and planning forums.
• Support TA Analytics Leads by sharing best practices, templates, and modeling approaches.

Qualifications

• Advanced degree in Statistics, Economics, Data Science, Operations Research, or a related quantitative field.
• Minimum 5 years of experience in pharmaceutical commercial analytics, with direct ownership of MMx / MMM and causal analysis, or advanced ML models.
• Proven experience supporting brand strategy, budget planning, and performance measurement.
• Hands-on expertise in advanced Marketing Mix Modeling, including: Bayesian and hierarchical modeling frameworks, Adstock, lag, and saturation modeling, Non-linear optimization and scenario simulation, Strong background in causal inference and incrementality measurement, such as: Geo-experiments and matched-market tests, Synthetic control methods, Uplift / incremental response modeling, Proficiency in Python and/or R for statistical modeling, feature engineering, and automation, Solid SQL skills and experience working with large, multi-source pharma datasets, Experience with cloud analytics platforms (e.g., Databricks, Snowflake) and production data pipelines, Familiarity with MLOps, version control, and model lifecycle management, Ability to translate analytical requirements into agent specifications, KBQ frameworks, and context layer definitions, Experience leading or executing UAT for analytics or AI-enabled platforms, including test plan design, execution, and stakeholder coordination
• Strong understanding of pharma commercial data (claims, APLD, specialty pharmacy, promotional, and digital engagement data).
• Experience integrating analytics with BI tools and decision workflows.
• Exposure to AI-enabled or automated analytics platforms is a plus (agentic systems not required but beneficial).
• Demonstrated ability to drive end-user adoption of new analytics or AI tools through training, enablement, and change management
• Ability to explain complex models and uncertainty to non-technical stakeholders.
• Strong collaboration skills across matrixed teams.
• Detail-oriented, with a focus on quality, reliability, and business impact.
• Agile mindset, with the ability to iterate quickly, reprioritize effectively.
• Comfortable working with ambiguity, able to make sound analytical and business judgments when problem definitions, data, or requirements are not fully defined
• Product-minded analytical thinker, able to bridge the gap between analytical rigor and user experience design