Associate Director, Commercial AI and Advanced Analytics
Bristol Myers Squibb
June 25, 2026
Remote friendly (Princeton, NJ)
United States
IT
Summary:
The Associate Director, Commercial AI and Advanced Analytics delivers advanced commercial measurement and decision intelligence across BMS brands and therapeutic areas, owning design, execution, and operationalization of Marketing Mix (MMx), causal, and optimization analytics to inform investment decisions, omnichannel strategy, and performance optimization.
Responsibilities:
- Own end-to-end development and execution of advanced Marketing Mix (MMx) and Key Driver Analyses (KDA) for brand planning, budget optimization, and performance assessment.
- Quantify incremental impact across channels (sales force, digital, TV, congresses, patient programs).
- Translate modeling outputs into business implications for brand strategy, media mix, and investment allocation; support planning, in-year optimization, and scenario analysis.
- Design/run advanced MMx models (e.g., Bayesian, Huber) including adstock/carryover, saturation/diminishing returns, distributed lag/non-linear response, and cross-channel interactions.
- Build scalable, repeatable MMx pipelines; integrate into self-service/agent-enabled analytics; ensure models are explainable and auditable for regulated pharma.
- Contribute to agentic and always-on insights; operationalize models into always-on pipelines with refresh cadence, data checks, and monitoring.
- Provide “what changed/why” and forward-looking recommendations using causal/driver methods; ensure transparency, lineage, and human-in-the-loop review.
- Define/maintain Key Business Questions (KBQ) framework and business context layer; partner on inputs/features/guardrails.
- Drive adoption via role-based training and structured feedback; act as primary business point of contact.
- Adhere to data privacy, AI governance, and model validation standards; maintain documentation; participate in reviews/audits.
- Partner with Brand/Marketing/Omnichannel/Field/Finance; present business-first narratives; share best practices with TA Analytics Leads.
Qualifications & Required Skills:
- Advanced degree in Statistics/Economics/Data Science/Operations Research or related field.
- 5+ years pharmaceutical commercial analytics with direct ownership of MMx/MMM and causal analysis or advanced ML models.
- Proven brand strategy, budget planning, and performance measurement experience.
- Advanced Marketing Mix Modeling (Bayesian/hierarchical; adstock/lag/saturation; non-linear optimization/scenario simulation).
- Causal inference/incrementality measurement (geo-experiments/matched-market tests; synthetic control; uplift/incremental response).
- Proficiency in Python and/or R; strong SQL; experience with large multi-source pharma datasets.
- Cloud analytics/platforms (e.g., Databricks, Snowflake) and production data pipelines; familiarity with MLOps and model lifecycle management.
- Ability to translate requirements into agent specs/KBQ/context layer definitions; lead/execute UAT.
- Ability to explain complex models/uncertainty to non-technical stakeholders; comfort with ambiguity and product-minded analytical thinking.
Domain/Platform Knowledge (Preferred/Plus):
- Pharma commercial data understanding (claims, APLD, specialty pharmacy, promotional, digital engagement).
- Experience integrating analytics with BI/decision workflows; exposure to AI-enabled/automated analytics platforms.
- Experience driving end-user adoption through training/enablement/change management.
The Associate Director, Commercial AI and Advanced Analytics delivers advanced commercial measurement and decision intelligence across BMS brands and therapeutic areas, owning design, execution, and operationalization of Marketing Mix (MMx), causal, and optimization analytics to inform investment decisions, omnichannel strategy, and performance optimization.
Responsibilities:
- Own end-to-end development and execution of advanced Marketing Mix (MMx) and Key Driver Analyses (KDA) for brand planning, budget optimization, and performance assessment.
- Quantify incremental impact across channels (sales force, digital, TV, congresses, patient programs).
- Translate modeling outputs into business implications for brand strategy, media mix, and investment allocation; support planning, in-year optimization, and scenario analysis.
- Design/run advanced MMx models (e.g., Bayesian, Huber) including adstock/carryover, saturation/diminishing returns, distributed lag/non-linear response, and cross-channel interactions.
- Build scalable, repeatable MMx pipelines; integrate into self-service/agent-enabled analytics; ensure models are explainable and auditable for regulated pharma.
- Contribute to agentic and always-on insights; operationalize models into always-on pipelines with refresh cadence, data checks, and monitoring.
- Provide “what changed/why” and forward-looking recommendations using causal/driver methods; ensure transparency, lineage, and human-in-the-loop review.
- Define/maintain Key Business Questions (KBQ) framework and business context layer; partner on inputs/features/guardrails.
- Drive adoption via role-based training and structured feedback; act as primary business point of contact.
- Adhere to data privacy, AI governance, and model validation standards; maintain documentation; participate in reviews/audits.
- Partner with Brand/Marketing/Omnichannel/Field/Finance; present business-first narratives; share best practices with TA Analytics Leads.
Qualifications & Required Skills:
- Advanced degree in Statistics/Economics/Data Science/Operations Research or related field.
- 5+ years pharmaceutical commercial analytics with direct ownership of MMx/MMM and causal analysis or advanced ML models.
- Proven brand strategy, budget planning, and performance measurement experience.
- Advanced Marketing Mix Modeling (Bayesian/hierarchical; adstock/lag/saturation; non-linear optimization/scenario simulation).
- Causal inference/incrementality measurement (geo-experiments/matched-market tests; synthetic control; uplift/incremental response).
- Proficiency in Python and/or R; strong SQL; experience with large multi-source pharma datasets.
- Cloud analytics/platforms (e.g., Databricks, Snowflake) and production data pipelines; familiarity with MLOps and model lifecycle management.
- Ability to translate requirements into agent specs/KBQ/context layer definitions; lead/execute UAT.
- Ability to explain complex models/uncertainty to non-technical stakeholders; comfort with ambiguity and product-minded analytical thinking.
Domain/Platform Knowledge (Preferred/Plus):
- Pharma commercial data understanding (claims, APLD, specialty pharmacy, promotional, digital engagement).
- Experience integrating analytics with BI/decision workflows; exposure to AI-enabled/automated analytics platforms.
- Experience driving end-user adoption through training/enablement/change management.