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About This Role
Summary \& Purpose of the Position
The Global Digital IT (GDIT) division is dedicated to being the business partner of choice for digital, data, and technologies, delivering outstanding value at speed and scale for patients and Ipsen. In this transformative context, GDIT plays a crucial role in driving innovation and efficiency across the organization.
Within GDIT, the Global Data \& AI Division was established with the mission to act as a business enabler. This department is responsible for driving the strategic use of Data and AI across Ipsen and enhance business interoperability.
The Data \& AI Product Delivery Lead for R\&D and Medical is a pivotal role within the Global Data \& AI Division. This position acts as the primary liaison between this division, R\&D and Medical Business Domains, and GDIT Business Partners for all data initiatives. The role ensures alignment with business priorities and maintains trusted relationships with stakeholders.
Key responsibilities include:
- Data Initiatives Liaison and Partnering: Facilitate communication and collaboration, ensuring alignment with business priorities.
- Strategic Roadmap and Portfolio Management: Create and manage data initiative roadmaps, balancing innovation and industrialization.
- Data Initiatives Delivery Supervision: Oversee the lifecycle of data initiatives, ensuring high\-quality delivery and alignment with business strategy.
- Managing Strategic Initiatives: Act as Product Manager and Delivery Lead, defining vision and managing roadmaps.
- Change Management: Design and execute change management plans to drive adoption and address resistance.
Main Responsibilities
Data initiatives liaison and partnering for R\&D and Medical
- Serve as the main point of liaison between the Global Data \& AI Division, R\&D and Medical Business Domains and GDIT Business Partners for all data initiatives in Global Data \& AI division scope, ensuring alignment with business priorities.
- Establish and maintain a trusted and transparent relationship with R\&D, Medical, GDIT Business partners, and the Global Data \& AI functions.
- Facilitate communication and collaboration through thought leadership, subject matter expertise, quality solution design, partnerships, and orchestration of relevant data initiatives delivery.
- Act as a technical expert and trusted advisor, enabling full utilization of Data \& AI platforms and Centers of Excellence.
- Ensure an integrated end\-to\-end focus on data \& AI solutions through the value chain.
Strategic Roadmap and Portfolio Management
- Collaborate with R\&D, Medical, GDIT Business Partners, and the Global Data \& AI functions to create data initiatives roadmaps and visions that address current and future business needs, balancing innovation and industrialization.
- Manage the portfolio of Data \& AI initiatives to accelerate the delivery of business value, focusing on long\-term, high\-impact projects that align with the organization’s strategic goals.
- Forecast and plan the delivery pipeline in alignment with business and delivery teams, prioritizing initiatives based on business value and feasibility.
Data initiatives delivery supervision
- Oversee the entire lifecycle of Data \& AI initiatives for R\&D and Medical, from initial demand to operational handover and drive adoption through the delivery of high\-quality and high\-value data factory initiatives.
- Collaborate with business functions to identify critical data use cases and translate business needs into data requirements, ensuring alignment with the overall business and GDIT strategy.
- Facilitate the definition and assessment of functional requirements, product specifications, architecture blueprints, data feasibility and best delivery options, ensuring the availability of necessary resources in line with initiative plans and budget.
- Ensure Global Data \& AI capabilities are fully leveraged to drive maximum business value and success.
- Build cross\-functional teams to address business needs and engage with all delivery partners to ensure on\-time, on\-budget, and on\-quality delivery, as well as value realization.
- Oversee MVP development, product scaling and operations, as well as security and compliance.
- Proactively identify and mitigate risks across all areas of responsibility.
- Collaborate with senior leadership and stakeholders at all levels, providing regular updates on progress, challenges, and successes.
- Participate in steering committees and manage governance meetings as relevant.
Strategic initiatives management
- Serve as the Product Manager and Delivery Lead for specific Data \& AI initiatives, defining vision, managing roadmaps, and overseeing product and initiative lifecycle from conception to delivery.
- Guarantee that delivered products and services align with customer needs and corporate objectives.
- Apply product development best practices, such as user\-centric design, agile methodologies, effective prioritization, cross\-functional collaboration, KPI measurement, product roadmaps, balancing technical debt, and fostering experimentation.
Data Management
- In collaboration with the other Data \& AI functions, oversee the quality, consistency, and integrity of R\&D and Medical data leveraged and delivered by Data \& AI initiatives
- Contribute to master data processes specific to R\&D and Medical, supporting compliance with data governance policies and frameworks for the Business Domain.
Change management
- Design and execute change management plans for data initiatives, including training, communication, and stakeholder engagement.
- Work closely with R\&D, Medical and GDIT Business Partners to drive adoption of new data initiatives and platforms across the organization and address concerns and resistance to change.
Knowledge \& Experience
- Master's degree in a relevant field such as Data Science, Computer Science, Information Technology, Business Administration, or related discipline.
- 7\+ years of experience in the management of data initiatives products, if possible, in Life Science
- Proven track record in managing cross\-functional, enterprise\-wide product initiatives.
- Proficient in English and French.
Technical knowledge:
- Experienced in R\&D \& Medical Data Transformation within the pharmaceutical industry
- Experienced in Project and Portfolio Management in the field of Data \& Analytics
- Experienced in Agile methodology and software development lifecycle
- Experienced in Change Management, including designing and executing change management plans
- Proficient in Data Analysis and Data Management, including data analysis tools (e.g., SQL, Python) and data management platforms
- Skilled in AI and Machine Learning
- Skilled in Software Engineering
- Skilled in Cloud Computing and Platforms, including AWS \& Azure
- Skilled with Collaboration Tools, such as JIRA \& Confluence
- Skilled in UX/UI design
- Capable in Technical Architecture and Solution Design
Knowledge \& Experience (preferred):
- Experience in the pharmaceutical or life sciences industry is a strong advantage.
- Familiar with Cloud Computing Platforms and enterprise architecture concepts.
- Skilled with Collaboration Tools such as JIRA, Confluence, and Microsoft 365\.
### The annual base salary range for this position is $150,000 \- $210,000\.
### This job is eligible to participate in our short\-term incentives program.
### At Ipsen we are proud to offer a comprehensive employee benefits package, including 401(k) with company contributions, group medical, dental and vision coverage, life and disability insurance, short\- and long\-term disability insurance, as well as flexible spending accounts. Ipsen also provides parental leave, paid time off, a discretionary winter shutdown, well\-being allowance, commuter benefits, and much more.
### The pay range displayed above is the range of base pay compensation within which Ipsen expects to pay for this role at the time of this posting. Individual compensation within this range depends on a variety of factors, including, but not limited to, prior education and experience, job\-related knowledge and demonstrated skills.
We are committed to creating a workplace where everyone feels heard, valued, and supported; where we embrace “The Real Us”. The value we place on different perspectives and experiences drives our commitment to inclusion and equal opportunities. When we include diverse ways of thinking, we make more thoughtful decisions and discover more innovative solutions. Together we strive to better understand the communities we serve. This means we also want to help you perform at your best when applying for a role with us. If you require any adjustments or support during the application process, please let the recruitment team know. This information will be handled with care and will not affect the outcome of your application. Ipsen is an equal opportunity employer that strictly prohibits unlawful discrimination. We recruit, employ, train, compensate, and promote without regard to an individual’s race, color, religion, gender, sexual orientation, gender identity/expression, national origin/ancestry, age, mental/physical disability, medical condition, marital status, veteran status, or any other characteristic protected by law.
Salary Context
This $150K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At IPSEN, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $150K to $210K.
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.
IPSEN AI Hiring
IPSEN has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cambridge, MA, US. Compensation range: $210K - $210K.
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/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 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).
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 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.
Frequently Asked Questions
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