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About This Role
Date posted: Sep 29, 2026 City: Boston Country/Region: US Type of Contract: Full-time Employment / Unlimited Job Requisition ID: 12871 US VP of Digital and Data with AI
About Servier
Servier in the U.S. is a Boston-based, commercial-stage biopharmaceutical company launched by Servier Group in 2018. As a privately held organization, Servier is uniquely positioned to advance cutting-edge science, tackle underserved therapeutic areas and make patients the focus of every strategic decision.
Role Summary
We have an exciting opportunity for a seasoned digital and data leader to shape how Servier US works, decides, and delivers—bringing clarity, accountability, and transparency to every step of the transformation journey. You will set a bold, business-led vision across Data & Analytics, Business Applications, and Digital Solutions, and translate it into a pragmatic roadmap that improves performance, elevates patient impact, and enables accelerated growth—while ensuring global alignment, technology convergence, and compliance.
As US Vice President of Digital and Data with AI (VP DDA), you will lead the Servier Pharmaceuticals US digital and data transformation and serve as a strategic partner to the US Leadership Team and functions across the affiliate. You will build trust through disciplined governance, clear decision-making, and transparent communication—so priorities are understood, progress is measurable, and outcomes are delivered.
Your mission is to accelerate Servier US’s digital transition and innovation in a growth environment—including acquisitions. You will ensure capabilities are consistent, efficient, compliant, and scalable, while remaining aligned to Servier’s global direction.
You will help business leaders anticipate how digital and data will shape strategy, operating model, and ways of working. You will guide the Business Relationship Management (BRM) capability to translate needs into prioritized initiatives—anchored in clear ownership, realistic plans, and measurable outcomes.
The VP DDA coordinates the overall transformation and, in close collaboration with the Global CIO and US DDIS leaders (Ops, Cyber, R&D, and Industry IT), ensures capabilities are delivered securely and coherently. You will champion standards and architecture discipline while enabling speed—so teams can innovate with clear visibility into risk, cost, and impact.
This role reports to the US CFO (direct) and the IS Regional Manager for the Americas (indirect) and partners closely with leaders across Finance, Procurement, and the broader US organization.
Objectives
- Set the US digital and data direction: Define the multi-year strategy across data & analytics, business applications, and digital solutions aligned to US priorities and Servier Group direction.
- Serve as a trusted partner to the US Leadership Team: Bring transparency to decision-making, clarify ownership, and ensure priorities and trade-offs are understood and aligned with business priorities.
- Accelerate adoption of digital, data, and AI: Drive scaled adoption across Commercial, Medical, R&D, Market Access, Corporate & Industry, and External Manufacturing to improve performance and outcomes.
- Strengthen data governance: Ensure data quality, stewardship, and compliant use of data across the US affiliate.
- Govern portfolio and architecture: Prioritize investments and ensure solutions align to standards and target architecture while meeting US needs.
- Align global/local ways of working: Navigate and harmonize US and global governance and standards; represent US needs in global forums and escalate constraints early.
- Lead change and capability building: Build a digital-first culture through change management, innovation, and upskilling.
- Optimize customer experience: Map and improve end-to-end digital journeys across channels and touchpoints, enabling consistent experiences and knowledge sharing.
- Demonstrate impact: Establish KPIs and ROI measures for digital investments and use insights to refine priorities and execution.
Responsibilities and Critical Success Factors
- Ability to deliver a clear digital and data strategy roadmap while enabling sustainable adoption across the US affiliate.
- Capacity to structure and transform teams amid organizational change and accelerating digital, data, and AI expectations.
- Strategic mindset with pragmatic execution—balancing portfolio delivery, value realization, and transformation.
- Ensure that digital initiatives are fully integrated with the strategic planning process, including leadership commitment and resource allocation.
Job Description
Candidate Profile
Education and Experience
- 15+ years in digital, data, and technology senior leadership roles, with experience across data/analytics, enterprise applications, and digital product delivery.
- Experience in pharma, biotech, or other regulated environments strongly preferred.
- Proven track record of digital transformation, operating model and organization redesign, and cross-functional leadership.
- Strong executive communication, stakeholder management, and change leadership.
- Technical expertise across data platforms, cloud (e.g., GCP), digital solutions, enterprise applications, and cybersecurity fundamentals.
- Ability to connect digital initiatives to measurable business value (revenue growth, productivity, risk reduction, and customer/patient impact).
Travel and Location
- Some domestic and international travel will be required (approximately once per quarter)
- This position is 3-4 days a week in the Boston office.
Servier’s Commitment Servier is committed to modeling diversity, equity, and inclusion within the industry. We are dedicated to fostering an environment that maintains equitable treatment for all and we welcome applicants who are passionate, committed, and innovative individuals. We encourage candidates to apply to our open roles as we are always willing to consider experiences and skills beyond what is listed in the job description.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Salary Range The salary range for this role is $275-$350k. An employee’s pay position within the salary range will be based on several factors including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs. We may ultimately pay more or less than the posted range, and the range may be modified in the future. Employees in this position are also eligible for Short-Term and Long-Term incentive programs. Servier also offers a competitive and comprehensive benefits package that includes benefits such as medical, dental, vision, flexible time off (Servier provides unlimited sick time and flex time, and does not accrue time off), 401(k), life and disability insurance, recognition programs among other great benefits (all benefits are subject to eligibility requirements). For more information on our benefits, please visit this link.
Nearest Major Market: Boston
Salary Context
This $275K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At SERVIER, 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 $212,000 based on 11,857 positions with disclosed compensation. This role's midpoint ($312K) sits 47% above the category median. Disclosed range: $275K to $350K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
SERVIER AI Hiring
SERVIER has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $350K - $350K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 tracked positions.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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