Senior Data Product Manager - Analytics & AI

Genentech•Daly City, CA

1d•$140,300 - $260,500•Hybrid

About The Position

The Data, Digital, and Analytics (DDA) team at Genentech is dedicated to solving complex healthcare challenges and improving patient outcomes. This team empowers business partners across Commercial, Medical, and Government Affairs (CMG) by leveraging data, analytics, business products, and AI/ML to enable fast, targeted actions in rapidly evolving business contexts. DDA fosters a unified understanding of customers, actions, and outcomes by integrating analytics and insights seamlessly into CMG’s evolving digital, data, and automation platforms, creating scalable solutions and eliminating silos. Within DDA, you will act as a trusted, objective advisor and expert, recommending critical decisions and actions with credibility and a focus on driving measurable impact, all within a thriving culture built on collaboration and innovation.

Requirements

Bachelor's degree in Business, Technology, Operations, Marketing, Data Science, Information Systems, Business Analytics, Computer Science, or a related field.
5+ years of combined experience in product management, data management, AI, software development, or equivalent experience, with a proven track record of driving strategic and tactical execution.
Strong technical fluency with big data technologies, relational and non-relational databases, SQL, and cloud-based data platforms (e.g., AWS, GCP, Azure) to effectively inform product and architectural decisions.
Strong working knowledge of Semantic Layer concepts, data lifecycle management, enterprise data governance frameworks, and FAIR data principles.
Proficient in modern data observability, data quality, governance, and lineage tools (e.g., Informatica Data Quality, Collibra, Monte Carlo).
Exceptional communication and presentation skills, with a demonstrated ability to evangelize product vision, navigate ambiguous corporate environments, and influence senior stakeholders without direct authority.
Proven experience collaborating with Legal, Compliance, and Privacy teams to ensure adherence to governance and regulatory standards.

Nice To Haves

Advanced degree such as an MBA or related graduate-level qualification.
Professional experience driving digital product operations within life sciences, or other complex, data-rich environments (e.g., technology, finance, CPG, or management consulting).
Relevant certifications or advanced training in agile product management, modern cloud data architecture, or enterprise data governance.
Experience with customer data strategy, enterprise omnichannel/digital marketing solutions, or complex CRM systems.
Experience leading enterprise data or systems projects to establish best practices, leveraging advanced knowledge of workflow tools and automation systems.

Responsibilities

Develop and communicate a clear, value-driven vision for forecasting data products and AI integration aligned with Genentech’s broader organizational objectives.
Partner with leadership to define comprehensive data product roadmaps, including predictive models, AI capability enhancements, timelines, and cross-product dependencies.
Lead end-to-end execution from strategy through implementation for a complex portfolio of Forecasting & AI Data Products.
Maintain and prioritize the product backlog to ensure it efficiently addresses both predictive forecasting needs and single or multi-business enterprise AI use cases.
Champion the product operating model and provide strategic AI context across business units.
Actively engage with business leaders and technical end-users (e.g., Data Scientists, AI/ML Engineers, and Analysts) to evangelize the forecasting vision, drive adoption, foster trust, and lead operational improvements within cross-functional data and AI squads.
Act as the primary strategic liaison between business stakeholders and the data product technology team.
Align stakeholders across multiple organizational levels, manage complex demand requests, and negotiate prioritization.
Establish and maintain a comprehensive enterprise data quality framework.
Define key metrics, identify root-cause issues, and partner with data stewards and engineers to ensure visibility into data integrity and performance.
Guide cross-functional teams in adhering to modern data architecture principles, including data mesh, data fabric, Semantic Layer concepts, and FAIR (Findable, Accessible, Interoperable, Reusable) data standards to ensure maximum scalability, including leading build-vs.-buy evaluations for data tooling.
Monitor system health, adoption rates, and product usage data in collaboration with Business and IT partners.
Drive product innovation through structured experimentation and data-driven decision-making to continuously maximize ROI.
Oversee compliance with overarching data governance policies and regulatory standards (e.g., HIPAA, GDPR).
Partner effectively with Legal, Compliance, and Privacy teams to ensure full adherence and mitigate risk.