Associate Director, Commercial AI Product Owner

Responsibilities:
- Serve as dedicated product owner for the Agentic MMx (Marketing Mix) Platform: own strategy, vision, roadmap, and stakeholder alignment; evolve self-service analytics hub (KDA, scenario simulation, investment planning, constrained optimization, real-time decision support).
- Lead the Tools & Automated Solutions cross-functional team (BI&T, Data Science, TA Analytics, analytics/engineering) to architect, launch, and maintain scalable analytics solutions (dashboards, always-on insights, scenario simulators, measurement pipelines, democratized KDA).
- Partner to deliver analytics-ready datasets, feature stores, semantic layers, and automated data pipelines to standardize and accelerate insight generation.
- Champion automation, templated workflows, and platformization to reduce manual effort and vendor reliance; identify tool innovation opportunities.
- Integrate decision science outputs into annual planning, CRM (e.g., Veeva), and omnichannel orchestration via APIs and embedded dashboards.
- Build monitoring/governance dashboards for model/data health, adoption, SLAs, drift/stability, and auditability.
- Architect/deploy autonomous and semi-autonomous analytics agents using multi-agent frameworks; own AI product lifecycle (POC→pilot→rollout→optimization) with safety/security/ethical/privacy governance.
- Lead cross-functional pods; manage roadmaps, agile backlogs, release cycles; standardize deployment/MLOps/CI-CD/QA, SLAs/SLOs, and RACI.
- Coach enterprise and TA-aligned analytics teams; promote agile ways of working (e.g., Jira).
- Partner on data/AI governance with Data Governance/Legal/Privacy; ensure explainability/auditability, bias monitoring, documentation, and human-in-the-loop where required; lead change management.

Qualifications:
- MS/PhD preferred in Data Science, Statistics, CS, Econometrics, or related quantitative field.
- 5+ years pharma commercial analytics or decision science.
- Causal inference/incrementality tools (geo-experiments, matched markets, synthetic controls, uplift modeling) and expertise in Bayesian/hierarchical MMx, adstock/distributed lag, saturation/response-curve modeling; operationalize in automated pipelines.
- Proven experience launching/scaling agentic AI (multi-agent, LLMs, RAG, semantic layers, real-time architectures) and agent orchestration.
- Experience operationalizing LLMs for commercial use cases (retrieval, summarization, generative analytics, automated insight generation).
- Enterprise experience embedding AI analytics platforms into commercial workflows.
- Strong pharma data knowledge (claims, APLD, specialty pharmacy, digital signals, promotional data).
- AI governance foundation in regulated environments (risk/security/privacy/model monitoring/human-in-the-loop).
- Cloud analytics (Databricks, Snowflake, Spark), MLOps, BI tools; HIPAA and GDPR/CCPA knowledge.
- Proficiency in Python/R, ML frameworks (scikit-learn, PyTorch), large-scale analytics, visualization, workflow automation.
- Familiarity with CRM (e.g., Veeva), omnichannel metrics, MTA, next-best-action.
- Strong stakeholder communication translating analytics into business strategy.

Application instructions:
- If the role isn’t a perfect match, you’re encouraged to apply anyway.