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. #J-18808-Ljbffr