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About This Role
Job title: Product Manager/Product Owner — Enterprise Analytics/AI/ML
Location: Morristown, NJ / Cambridge, MA
About the job
Ready to push the limits of what’s possible? Join Sanofi in one of our corporate functions and you can play a vital part in the performance of our entire business while helping to make an impact on millions around the world.
Sanofi is going through a vast and ambitious digital transformation program. A cornerstone of this roadmap is the acceleration of its data transformation and of the adoption of artificial intelligence (AI) and machine learning (ML) solutions, to accelerate R\&D, manufacturing and commercial performance and bring better drugs and vaccines to patients faster, to improve health and save lives.
A core pillar of this strategy is the modernization of our US Gross‑to‑Net (GTN) capabilities—moving from manual, fragmented processes to AI‑powered, connected, end‑to‑end GTN insights, forecasting, reconciliation, and decision support. We are seeking a highly skilled and experienced Product Owner to lead a portfolio of AI and analytics products that power the US GTN function. This includes three strategic GTN products:
- GTN CLARITY – retrospective, Rx‑level GTN insights supporting near‑term GTN decisions.
- GTN COMPASS – forward‑looking GTN investment strategy powered by predictive analytics.
- Dynamic GTN Forecasting – predictive and dynamic GTN forecasting using advanced ML \& AI techniques.
You will own the product vision, strategy, and roadmap for this suite of GTN AI solutions, ensuring alignment with Finance, Pricing, Access Analytics, Go to Market Capabilities (GTMC), Digital, and Brand leadership teams. The ideal candidate has deep expertise in AI/ML‑driven analytics platforms, strong product leadership experience, and the ability to shape high‑impact digital solutions for complex, regulated, and data‑intensive business functions. You will play a critical role in shaping our Advanced analytics \& AI solution development, delivery, and overall digital transformation; implementing best practices (including responsible AI), and driving strategic initiatives to ensure the reliability, scalability – continuously improving \& challenging the status quo.
Our Vision for Digital, Data Analytics and AI
Join us on our journey in enabling Sanofi's Digital Transformation through becoming an AI first organization. This means:
- AI Factory \- Versatile Teams Operating in Cross Functional Pods: Utilizing digital and data resources to develop AI products, bringing data management, AI and product development skills to products, programs, and projects to create an agile, fulfilling, and meaningful work environment.
- Leading Edge Tech Stack: Experience building products that will be deployed globally on a leading\-edge tech stack.
- World Class Mentorship and Training: Working with renowned leaders and academics in machine learning to further develop your skillsets.
As a Product Owner, you will be a leader and act as a mini\-CEO for this digital product, co\-defining product strategy with the business, planning product roadmap, driving product delivery with delivery team, and measuring/optimizing business impact.
We are an innovative global healthcare company with one purpose: to chase the miracles of science to improve people's lives. We're also a company where you can flourish and grow your career, with countless opportunities to explore, make connections with people, and stretch the limits of what you thought was possible. Ready to get started?
About Sanofi
We’re an R\&D\-driven, AI\-powered biopharma company committed to improving people’s lives and delivering compelling growth. Our deep understanding of the immune system – and innovative pipeline – enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve people’s lives.
Main Responsibilities:
- Lead End\-to\-End Product Delivery: Own the full product lifecycle — from discovery and requirements definition through build, test, and scale — for GTN CLARITY, GTN COMPASS, and Dynamic GTN Forecasting. Drive delivery excellence across cross\-functional pods including Data Engineering, Data Science, UX, and AI teams.
- Define and Execute Product Vision: Establish and maintain a clear, compelling product vision and multi\-horizon roadmap for a connected GTN analytics ecosystem. Translate complex business challenges into well\-defined, prioritized product backlogs that deliver measurable value.
- Drive Enterprise Data Transformation: Lead the migration and modernization of fragmented, manual GTN processes onto scalable, AI\-ready data foundations. Champion adoption of enterprise data standards (USDF, OneMesh) and ensure architectural decisions support long\-term scalability across brands and BUs.
- Optimize and Prioritize Use Cases: Identify, assess, and prioritize GTN use cases (retrospective insights, prospective modeling, forecasting, reconciliation, monitoring) based on business value, technical feasibility, and data readiness. Apply structured frameworks to manage trade\-offs and sequencing.
- Collaborate with Business Stakeholders: Partner with Finance, Pricing, Access Analytics, GTN CoE, Brand GMs, and GTMC teams to deeply understand pain points, co\-design solutions, and ensure GTN tools support key governance processes including Actuals Review, Access \& Investment Boards, Strategic Access Forums, and bid\-cycle preparation.
- Manage Vendor and Platform Selection: Lead RFP processes, vendor evaluation, and Systems Integrator selection for Dynamic GTN Forecasting platform. Manage external delivery partners and ensure alignment with Sanofi's enterprise architecture and data governance standards.
- Standardize and Scale Digital Products: Drive standardization across GTN workflows, dashboards, forecasting logic, and reconciliation engines to ensure consistency and reuse across brands and BUs. Scale CLARITY to new brands and expand COMPASS capabilities as data foundations mature.
- Ensure Performance and Value Realization: Define KPIs, establish measurement frameworks, and ensure measurable business impact through improved GTN accuracy, reduced variance, automation of manual workflows, and enhanced strategic decision\-making.
- Champion Responsible AI: Ensure all GTN AI/ML solutions are developed and deployed in alignment with Sanofi's RAISE responsible AI framework, maintaining compliance, auditability, and transparency across all products.
Ways of working:
- Member of the Global Digital GBU Advanced Analytics \& AI Team operating in cross\-functional pods.
- Foster partnerships and close collaboration between cross\-functional teams (GBU Brand teams, Digital, business operations, market teams) driving structured ways of working to maximize value and efficiency.
- Work closely with development teams (Accelerator, AI COEs, Data Engineering COE, Responsible AI COE) whether inside or outside the company.
- Empower teams to set up lean but effective delivery structures and efficient local governance.
- Build and develop teams that use complementary strengths to deliver novel solutions to core business problems.
About You
Job Skills \& Competencies
- Bachelors degree with 7\+ years of experience in digital product management, with a strong track record of delivering end\-to\-end analytics platforms, data products, or AI/ML solutions at enterprise scale \- required.
- 5\+ years leading complex digital transformation programs in large, matrixed organizations — ideally spanning data engineering, advanced analytics, and AI/ML product development \- required.
- Demonstrated ability to translate ambiguous, complex business problems into structured product roadmaps and scalable technical solutions.
- Proven experience managing multi\-vendor delivery environments including RFP processes, Systems Integrator selection, and external partner management.
- Strong understanding of enterprise data architecture concepts including data mesh, data lakes, cloud platforms, and connected data foundations.
- Exceptional stakeholder management skills with executive\-level communication and the ability to influence without authority across business and technology functions.
- Familiarity with financial analytics, forecasting, or reconciliation domains — experience in pharmaceutical commercial analytics a plus but not required.
Technical skills:
- Deep knowledge of Lean/Agile practices, product management frameworks, and UX/UI principles.
- Hands\-on experience with data engineering and analytics stacks (Python, SQL, Spark, cloud data lakes — Azure, AWS, GCP).
- Strong understanding of AI/ML solution design, predictive modeling concepts, and analytics platform architecture.
- Experience with forecasting frameworks, scenario modeling, variance decomposition, and reconciliation workflows in complex data environments.
- Familiarity with enterprise data platforms (Snowflake, Databricks) and connected data architecture standards.
- Ability to work with AI\-driven insights, natural language interfaces, and ML\-enabled forecasting tools.
- Experience with EPM platforms (Anaplan, Adaptive Insights, or similar) a strong plus.
Soft skills:
- Strategic thinker with strong business intuition and ability to connect technical solutions to business outcomes.
- Influential leader who inspires cross\-functional teams and drives alignment across competing priorities.
- Strong problem solver with a "get things done" mindset and comfort operating in ambiguous, fast\-moving environments.
- Excellent communicator who can translate complex technical concepts for non\-technical audiences and vice versa.
Languages: English a must, French a plus, other languages a plus
Why Choose Us?
- Bring the miracles of science to life alongside a supportive, future\-focused team.
- Discover endless opportunities to grow your talent and drive your career, whether it’s through a promotion or lateral move, at home or internationally.
- Enjoy a thoughtful, well\-crafted rewards package that recognizes your contribution and amplifies your impact.
- Take good care of yourself and your family, with a wide range of health and wellbeing benefits including high\-quality healthcare, prevention and wellness programs and at least 14 weeks’ gender\-neutral parental leave.
Sanofi US Services and its U.S. affiliates are Equal Opportunity employers committed to a culturally inclusive workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status; domestic violence victim status; atypical cellular or blood trait; genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law.
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All compensation will be determined commensurate with demonstrated experience. Employees may be eligible to participate in Company employee benefit programs, and additional benefits information can be found here.
Salary Context
This $178K-$257K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Sanofi, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. Disclosed range: $178K to $257K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Sanofi AI Hiring
Sanofi has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in Morristown, NJ, US. Compensation range: $232K - $257K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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