Associate Director, Omnichannel Engine Business Product Owner

Working with Us
Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible.

Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us.

Summary:

This person will help design, scale, and operate BMS’s agentic omnichannel orchestration and next‑best‑engagement engine as a core enterprise platform.

This role operates at the intersection of data science, machine learning engineering, and commercial execution, translating brand and Therapeutic Area strategies into operational decision logic, ML‑powered recommendations, and closed‑loop automation that guide actions, content, and channel selection at the individual customer level (N=1).

Serving as a key delegate to the Director, Omnichannel Engine Business Product Owner, the Associate Director collaborates closely with data scientists, ML engineers, and platform teams to define the decisioning and automation roadmap, support model design and deployment, and ensure agentic workflows are integrated with CRM, marketing automation, and measurement systems. This role also plays an important part in ensuring model interpretability, governance, and continuous learning across Oncology, Cardiovascular, Neurology, and other portfolios.

Responsibilities:

Agentic Omnichannel Engine Product Strategy

• Support ownership of the product vision and technical roadmap for the omnichannel orchestration and next‑best‑engagement engine, with emphasis on agentic decisioning and ML‑driven automation.
• Translate commercialization priorities and Therapeutic Area strategies into decisioning capabilities, inference patterns, and orchestration models.
• Continuously evolve the roadmap based on model performance, learning velocity, adoption, and advances in agentic AI.

Design of Agentic Decisioning & Machine Learning Logic

• Work hands‑on with data scientists and ML engineers to design and operationalize:
  • Predictive, uplift, response, and recommendation models
  • Agent‑based decision flows and hierarchical prioritization logic
  • Optimization objectives, constraints, and guardrails embedded into inference
• Define model input requirements, feature availability, refresh cadence, inference latency targets, and fallback logic.
• Ensure ML outputs are translated into auditable, and actionable recommendations consumable by field and digital channels.

Translation of Brand Strategy into Engine Logic

• Partner with Brand and Therapeutic Area teams to convert customer strategies into formalized business rules, triggers, eligibility logic, and prioritization schemas.
• Encode channel‑specific nuances across field, digital, patient, medical, and access contexts.
• Balance rules‑based logic and probabilistic models to ensure performance, explainability, and compliance.

Orchestration Workflow & Automation Design

• Define and maintain agentic orchestration workflows governing recommendation generation, prioritization, and delivery.
• Partner with CRM and Marketing Automation engineering teams to enable low‑latency, scalable execution across Salesforce, Veeva, and related platforms.
• Implement champion–challenger frameworks, A/B testing, and continuous experimentation within orchestration logic.

Closed‑Loop Learning & Measurement Integration

• Architect closed‑loop feedback mechanisms linking recommendations issued, actions executed, and customer responses.
• Partner with Business Insights, Measurement, and Data Engineering teams to define telemetry, diagnostics, and performance KPIs (e.g., adoption, decision quality, incremental lift).
• Apply learnings to continuously tune models, features, thresholds, and orchestration rules.
• Architect closed‑loop feedback mechanisms linking recommen