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About This Role
Job title: Director of Franchise Digital Strategy & AI Innovation
- Location: Morristown, NJ
About the Job
Sanofi is seeking a Director of Franchise Digital Strategy & AI Innovation to lead precision marketing and content strategy across HCP and patient segments, driving measurable commercial impact within a dedicated franchise team. This role sits at the intersection of AI-powered innovation and customer-centric content strategy, serving as an embedded strategic partner to franchise leadership. The ideal candidate will leverage advanced AI/ML technologies to optimize segmentation, personalize engagement, and scale content strategies that accelerate franchise growth.
Join the team protecting half a billion lives every year with next-gen science, mRNA innovation, and AI driven breakthroughs. In Vaccines, you’ll help advance prevention on a global scale - and shape the future of immunization.
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
- Precision Marketing Strategy: Develop and execute segment-specific marketing strategies using data-driven audience insights, predictive targeting, and AI-powered personalization to optimize HCP and patient engagement across all touchpoints.
- Content Strategy Across Segments: Lead the creation and governance of content strategies tailored to distinct customer segments, ensuring relevant, compliant, and high-impact messaging across channels and therapeutic areas.
- AI & ML Innovation: Champion the integration of AI/ML technologies — including recommendation engines, NLP-driven content optimization, and predictive analytics — to enhance marketing effectiveness and personalization at scale.
- Performance & Analytics: Define KPIs and measurement frameworks to track segmentation effectiveness, content performance, and ROI; deliver regular insights to franchise leadership.
- Cross-Functional Partnership: Collaborate with Commercial, Medical Affairs, Regulatory, and IT teams to align digital and content strategies with franchise business objectives.
About You
- Bachelor's degree in Marketing, Business, Digital Strategy, Computer Science, or related field
- 10+ years of Progressive experience in digital strategy or precision marketing, with at least 5 years in leadership roles
- 5+ years of experience in AI/ML implementation within marketing or content contexts
- 5+ years in healthcare, pharmaceutical, or life sciences industry
- Proven track record delivering measurable business results through segmentation and content strategy
- MBA or advanced degree preferred
- AI/ML applications: predictive analytics, NLP, recommendation engines, and personalization platforms
- Customer segmentation tools and audience analytics platforms
- Content management systems and digital asset management
- Marketing automation and CRM platforms (e.g., Veeva, Salesforce)
- Customer data platforms (CDPs) for precision targeting
- Omnichannel marketing strategy and execution
- Data analytics and business intelligence tools
- Healthcare regulatory compliance (FDA, EMA)
- Demonstrated success managing cross-functional teams within franchise or brand structures
- Track record of building high-performing digital and content strategy teams
- Experience presenting business performance and digital strategy outcomes to senior and C-level leadership
- Proven ability to influence and drive change across large, matrixed organizations
Preferred Qualifications
- MBA or advanced degree in a relevant field
- Experience with pharmaceutical product launches and lifecycle management
- Background in management consulting or digital transformation with a growth focus
- Certification in project management (PMP) or agile methodologies
Key Competencies
- Strategic Thinking:Translates franchise business objectives into actionable precision marketing roadmaps
- AI Innovation Leadership: Drives adoption of AI/ML solutions to scale personalization and content optimization
- Content Strategy: Develops segment-specific content frameworks that resonate across HCP and patient audiences
- Data-Driven Decision Making: Leverages analytics and segmentation insights to optimize targeting and measure impact
- Customer-Centricity: Deep understanding of HCP and patient behaviors within the franchise's therapeutic area
- Collaborative Leadership:Builds alignment across diverse stakeholders while maintaining focus on franchise goals
- Results Orientation: Relentless focus on measurable outcomes, ROI, and continuous performance improvement
- Agility & Adaptability: Pivots strategies quickly based on market signals, data insights, and business needs
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.
- Work at the forefront of AI-driven research, smart manufacturing, and groundbreaking mRNA platforms that are redefining the future of immunology.
- Help protect half a billion people every year, contributing to one of the largest global vaccination efforts in healthcare.
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/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Sanofi, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. Disclosed range: $178K to $257K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Sanofi AI Hiring
Sanofi has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Morristown, NJ, US, Cambridge, MA, US, Morristown, NJ. Compensation range: $124K - $257K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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.
Frequently Asked Questions
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