Manager, Commercial AI Product Owner

Responsibilities

• Product co-ownership: Serve as dedicated product co-owner for the Agentic MMx (Marketing Mix) and Always on Insights (AOI) Platform - partner on its strategy, vision, roadmap, and stakeholder alignment in collaboration with BIT and Data Science. Drive the ongoing evolution of this self-service analytics hub to deliver advanced capabilities, including MMx, scenario simulation, investment planning, constrained optimization, and real-time decision support.
• Workflow integration: Partner with BIT to integrate decision-science outputs into annual planning, CRM (e. g. , Veeva), and omnichannel orchestration workflows via APIs and embedded dashboards to ensure sustained adoption and business impact.
• Monitoring oversight: Support monitoring and governance dashboards for real-time oversight of model/data health, user adoption, SLAs, drift/stability, and auditability.
• Collaboration: Partner with BIT, Data Science, TA Analytics, and engineering to deliver analytics-ready datasets, feature stores, semantic layers, and automated data pipelines, standardizing and accelerating insight generation.
• Efficiency and Innovation: Champion automation, templated workflows, and platform creation to reduce manual effort and external vendor reliance, maximizing reuse and operational efficiency. Continuously identify opportunities for tool innovation and scalable enablement of best practice measurement solutions.
• Product KPIs enablement: Define and track product KPIs-adoption, activation, retention, and model accuracy vs. business lift-and build training content, enablement, and adoption playbooks to drive sustained usage and value realization.

Agentic AI Capability Development

• Support the design and deployment of autonomous and semi-autonomous analytics agents using multi-agent frameworks, enabling progression from descriptive analytics to causal analysis, root-cause insights, and predictive recommendations.
• Contribute to the AI product lifecycle-proof-of-concept, pilot, rollout, and continuous optimization-while helping establish governance covering safety, security, ethical standards, and privacy compliance.
• Operationalize LLMs for commercial use cases such as knowledge retrieval, summarization, generative analytics, and automation of insight generation.

Stakeholder Partnership Strategic Support

• Partner closely with US Commercial stakeholders, Global Analytics, OCx, Marketing, UX/Design, and BIT to understand business needs, scope analytical solutions, and design impactful deliverables.
• Act as a strategic thought partner-helping stakeholders frame decisions, interpret insights, and understand trade-offs and implications.
• Articulate ROI and business cases to guide AI investment, prioritization, and resource-allocation decisions-quantifying expected business lift against cost, effort, and risk.
• Collaborate closely with UX/Design to shape intuitive, user-centered product experiences that maximize adoption, ease of use, and stakeholder trust.
• Manage and prioritize competing business demands across time zones to ensure alignment with strategic objectives and effective demand management for the Hyderabad hub.

Technical Oversight, Governance, Compliance Trust

• Oversee all technical tasks performed by the analytics team, including dashboards, business rules, documentation, automated pipelines, and model governance.
• Coordinate with BIT teams to ensure reliable, timely, and scalable data flows into downstream analytics and reporting solutions.
• Partner with Data Governance, Legal, and Privacy teams to apply data/AI governance: SLAs, RACI, privacy-by-design, and responsible/ethical AI controls.
• Ensure analytical outputs are well-documented, reproducible, explainable, auditable, and compliant-monitoring for bias and incorporating human-in-the-loop mechanisms where required.

Required Qualifications

Education Experience

• Advanced degree (MS/PhD) preferred in Data Science, Statistics, Computer Science, Econometrics, or a related quantitative field; a BA/BS with strong relevant experience will be considered.
• 7+ years of hands-on experience in pharma commercial analytics or decision science, including experience developing and shipping production-grade AI led analytical solutions.
• Experience managing, mentoring, and developing analytics talent, and leading cross-functional pods in a global enterprise environment.
• Hands-on experience building and scaling agentic AI solutions (multi-agent systems, LLMs, RAG, semantic layers, real-time architectures).
• Experience embedding AI analytics platforms into commercial workflows with enterprise-wide adoption.
• Extensive knowledge of pharmaceutical data (claims, APLD, specialty pharmacy, digital signals, promotional data).
• Product management/product owner (PM/PO) certification (e. g. , CSPO, SAFe PO/PM, Pragmatic Institute) a plus.

Core Competencies

• Outstanding stakeholder engagement and communication skills-capable of translating complex analytics concepts into actionable business strategies.
• Strong project management and interpersonal skills, with the ability to lead diverse teams and manage multiple competing priorities.
• Strong analytical and creative problem-solving skills, with the ability to synthesize insights from disparate data sources.
• Strategic mindset with a passion for hands-on, AI-led innovation and continuous improvement.

Technical Skills

• Expertise of programming and data science tools (Python, R), machine learning frameworks (scikit-learn, PyTorch), large-scale analytics environments, visualization platforms, and workflow automation.
• Proficiency with causal inference and incrementality tools (geo-experiments, matched markets, synthetic controls, uplift modeling); expertise in Bayesian/hierarchical MMx, adstock/distributed lag, and saturation/response-curve modeling, and operationalizing these in automated pipelines and platforms.
• Deep proficiency in enabling agent collaboration, negotiation, and task orchestration, including coordination of agent roles/functions within a commercial analytics or decision-science context.
• Experience operationalizing LLMs for commercial use cases-knowledge retrieval, summarization, generative analytics, and automation of insight generation.
• Experience with cloud analytics platforms (Databricks, Snowflake, Spark), MLOps, and BI tools.
• Strong foundation in AI governance (risk management, security, privacy, model monitoring, human-in-the-loop) in regulated environments; understanding of HIPAA, GDPR/CCPA and regulatory standards.
• Familiarity with CRM (e.g., Veeva), omnichannel metrics, multi-touch attribution (MTA), and AI-driven next-best-action frameworks.

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