Associate Director, Commercial AI and Advanced Analytics- Oncology
for each section heading. Use - for lists. Use
for prose.
We need to preserve all substantive information, not invent or summarize away detail. Remove boilerplate etc. We need to include the content from the posting, but we can condense into lists where appropriate, but must preserve all detail. That's tricky: we need to keep all details but can format as lists. We must not omit any substantive info. Let's extract key info.
First, About the role: The summary paragraph: "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." Then "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. This role owns the design, execution, and operationalization of advanced Marketing Mix (MMx), causal, and optimization analytics that inform investment decisions, omnichannel strategy, and performance optimization. This position is highly hands‑on and execution‑focused, combining strong technical depth in marketing analytics with the ability to translate complex modeling outputs into clear, actionable recommendations for Marketing, Brand, Omnichannel, Field, and Finance partners. The Associate Director operates within an enterprise analytics framework, aligning with TA Analytics Leads and centralized platforms while ensuring high‑quality, on‑time delivery of insights that directly impact brand strategy and ROI."
We can combine into a paragraph.
Responsibilities: Several bullet-like paragraphs. We'll convert each sentence or clause into
- . Must preserve all details.
Responsibilities sections:
- 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, etc. 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: includes many bullet points. We'll list them.
Qualifications paragraph: "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. Ability to explain complex models and uncertainty to non‑technical stakeholders. 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."
Domain & Platform Knowledge: "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."
We could combine Qualifications and Skills into one section "Qualifications" maybe with sub-sections? But spec says use headings for section headings like Qualifications, Skills. We could have both Qualifications and Skills sections. However we need to avoid thin sections. We could combine qualifications and skills into one Qualifications section, but spec says we can have Skills heading if there is content. The posting includes skills like proficiency in Python/R, SQL, cloud platforms, MLOps, etc. Those are skills. We could separate: Qualifications (education, experience) and Skills (technical). But we must not create thin sections; but we have enough content for both.
Let's decide: Qualifications heading: education, years experience, proven experience supporting brand strategy, etc. Skills heading: technical proficiencies (Python/R, SQL, cloud platforms, MLOps, etc.) plus domain knowledge.
But we also have Domain & Platform Knowledge which could be part of Skills.
We'll produce:
About the role
... (combined summary)
Responsibilities
- ... (list items as above)
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.
Skills
- 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.
- Ability to explain complex models and uncertainty to non‑technical stakeholders.
- 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.
- 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.
Benefits
Subject to the terms and conditions of the applicable plans then in effect, you may be eligible to participate in our comprehensive benefit plans – including wellbeing support, retirement and financial protection benefits, and insurance offerings (medical, dental, vision, life and disability). U.S.-based exempt employees are eligible for Flexible Time Off (FTO), which provides paid time off without a set accrual limit, subject to manager approval, along with 11 paid company holidays each year. Non-exempt employees, RayzeBio employees, and employees located in Puerto Rico receive 160 hours of paid vacation annually for new hires (subject to manager approval), 11 paid company holidays, and 3 optional holidays. Depending on eligibility, employees may also have access to additional time-off benefits, including paid sick leave, up to two paid volunteer days per year, summer hours flexibility, and leaves of absence for medical, personal, parental, caregiver, bereavement, or military needs. Eligible employees also enjoy an annual Global Shutdown between Christmas Day and New Year's Day. U.S.-based job seekers can explore full benefit offerings
for prose. We need to preserve all substantive information, not invent or summarize away detail. Remove boilerplate etc. We need to include the content from the posting, but we can condense into lists where appropriate, but must preserve all detail. That's tricky: we need to keep all details but can format as lists. We must not omit any substantive info. Let's extract key info. First, About the role: The summary paragraph: "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." Then "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. This role owns the design, execution, and operationalization of advanced Marketing Mix (MMx), causal, and optimization analytics that inform investment decisions, omnichannel strategy, and performance optimization. This position is highly hands‑on and execution‑focused, combining strong technical depth in marketing analytics with the ability to translate complex modeling outputs into clear, actionable recommendations for Marketing, Brand, Omnichannel, Field, and Finance partners. The Associate Director operates within an enterprise analytics framework, aligning with TA Analytics Leads and centralized platforms while ensuring high‑quality, on‑time delivery of insights that directly impact brand strategy and ROI." We can combine into a paragraph. Responsibilities: Several bullet-like paragraphs. We'll convert each sentence or clause into
About the role
... (combined summary)
Responsibilities
- ... (list items as above)
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.
Skills
- 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.
- Ability to explain complex models and uncertainty to non‑technical stakeholders.
- 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.
- 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.
Benefits
Subject to the terms and conditions of the applicable plans then in effect, you may be eligible to participate in our comprehensive benefit plans – including wellbeing support, retirement and financial protection benefits, and insurance offerings (medical, dental, vision, life and disability). U.S.-based exempt employees are eligible for Flexible Time Off (FTO), which provides paid time off without a set accrual limit, subject to manager approval, along with 11 paid company holidays each year. Non-exempt employees, RayzeBio employees, and employees located in Puerto Rico receive 160 hours of paid vacation annually for new hires (subject to manager approval), 11 paid company holidays, and 3 optional holidays. Depending on eligibility, employees may also have access to additional time-off benefits, including paid sick leave, up to two paid volunteer days per year, summer hours flexibility, and leaves of absence for medical, personal, parental, caregiver, bereavement, or military needs. Eligible employees also enjoy an annual Global Shutdown between Christmas Day and New Year's Day. U.S.-based job seekers can explore full benefit offerings