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About This Role
Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
Location
Must be local or willing to relocate to New York
About the Job You're Considering
Capgemini’s Insights & Data (I&D) practice is seeking a Life Sciences Data & AI Go-to-Market (GTM) Leader to drive growth, sales, and market expansion across the Pharmaceuticals, Biotechnology, MedTech, and Healthcare sectors.
This GTM leadership role is responsible for shaping and executing the Life Sciences Data & AI go-to-market strategy, developing industry-focused offerings, leading complex pursuits, building executive client relationships, and driving revenue growth. The role combines business development, offer strategy, partner ecosystem leadership, and thought leadership to position Capgemini as a trusted advisor in Data, Analytics, AI, and Generative AI transformation.
Capgemini’s 30,000-person strong Insights and Data practice builds platforms for disruptive business models & new market growth and realizes business value for our clients through AI, data and analytics. Our Insights & Data Global Business Line partners with our clients to identify, build and carry through transformation programs that improve growth and sharpen competitive edge on a long-term basis. Our teams bring industry, process and technology expertise and methods, tools and experience for advancing global, complex deployments.
Our Americas practice has an aggressive growth trajectory and as a premier partner of many of the key technology players, we are positioned to achieve that growth quickly. Our growth ambition and collaborative approach to working with clients combine to create a business culture that allows new colleagues to dive right in and become an integral part of the team immediately. The entrepreneurial working style and size of our practice allows you to create a name for yourself, shine early on and have a significant impact on our clients and on our company
A GTM Lead manages a team of salespeople within a defined industry sector, business line and/or geography. He/she also personally supports salespeople in driving sales opportunities from detection to closure, helping them find internally the relevant resources they may need. He/She is accountable for the quality of sales data and provides accurately sales forecast consolidating a vision of his team sales activity, according to a budget he sets up to each sales individual He takes care of the motivation of his teams and support its members in defining and achieve their professional develpment. Masters negotiations and consulting skills e.g., ASE to align clients and sponsors in the closing of a deal Is able to negotiate at CxO level for strategic deals Goes beyond closing deals and tries to build long term relationships with clients, acting as an advisor vs. salesperson Thinks big deals and mega deals shaping to meet new expectations and create value for both the client and Capgemini, but also being comfortable with walking away from unqualified deals Clearly communicates expectations towards sales team members, supports in finding sales opportunities, creates the conditions to success and manages commitment
Your Role
- Define and execute the Life Sciences Data & AI growth strategy across R&D, Clinical, Commercial, Supply Chain, Regulatory, and Quality functions.
- Develop and position differentiated offerings leveraging Data & AI, Generative AI, Agentic AI, Data Management, Cloud Modernization, and Advanced Analytics.
- Own the sales lifecycle, including pipeline generation, opportunity qualification, pursuit strategy, proposal development, executive presentations, and deal closure.
- Lead strategic pursuits and support RFP/RFI responses, client workshops, and business case development.
- Build trusted advisor relationships with executive stakeholders including Chief Data & Analytics Officers, Digital Transformation leaders, R&D IT leadership, and Commercial Analytics executives.
- Collaborate with account teams, delivery leadership, and Centers of Excellence to align solutions, talent, and delivery capabilities.
- Develop and manage strategic alliances with technology partners including Microsoft, AWS, Google Cloud, Databricks, Snowflake, Veeva, and emerging GenAI ecosystem partners.
- Represent Capgemini at industry conferences, partner events, client forums, and thought leadership initiatives.
- Create industry points of view, whitepapers, and executive content showcasing Capgemini’s Data & AI capabilities within Life Sciences.
- Mentor and enable client-facing teams on Life Sciences Data & AI trends, solutions, and market positioning.
Your Skills and Experience
- 15+ years of experience in consulting, technology sales, business development, solution sales, or go-to-market leadership.
- Significant experience within the Life Sciences industry, including Pharmaceuticals, Biotechnology, MedTech, or Healthcare.
- Proven success building pipeline and closing large-scale Data & AI, Analytics, Cloud, and AI transformation engagements, typically valued at $1M+.
- Strong understanding of Life Sciences business processes and regulatory frameworks, including GxP, 21 CFR Part 11, HIPAA, and data compliance considerations.
- Experience selling and positioning solutions across:
- + Data & Analytics + Cloud Data Platforms + Data Governance & Master Data Management + Artificial Intelligence & Machine Learning + Generative AI & Large Language Models + MLOps and Data Modernization
- Strong consultative selling, executive relationship management, negotiation, and presentation skills.
- Demonstrated ability to work across sales, delivery, partner, and solution organizations in a matrixed global consulting environment.
- Experience developing strategic partnerships and co-sell motions with cloud hyperscalers and technology vendors.
- Bachelor's degree required; MBA or advanced degree preferred.
Preferred Qualifications
- Established network of Life Sciences client relationships and executive decision-makers.
- Experience with industry platforms such as Veeva, Medidata, IQVIA, or other Life Sciences data ecosystems.
- Experience leading Data & AI initiatives supporting clinical development, drug discovery, commercial analytics, real-world evidence, regulatory operations, or supply chain transformation.
- Previous quota-carrying sales leadership, practice leadership, or P&L responsibility.
- Strong knowledge of emerging technologies including Generative AI, Agentic AI, Data Mesh, Modern Data Platforms, and Cloud-native analytics solutions.
The base compensation range for this role in the posted location is $200,000 - $215,000
Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.
The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.
These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.
It is not typical for candidates to be hired at or near the top of the posted compensation range.
In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.
Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:
- Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
- Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
- Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
- Life and disability insurance
- Employee assistance programs
- Other benefits as provided by local policy and eligibility
Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini’s discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation.
Disclaimers
Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.
This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact.
Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process.
Click the following link for more information on your rights as an Applicant in the United States. http://www.capgemini.com/resources/equal-employment-opportunity-is-the-law
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.
Salary Context
This $200K-$215K range is above the median 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 Capgemini, 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. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $200K to $215K.
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.
Capgemini AI Hiring
Capgemini has 34 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, MLOps Engineer, AI Consultant. Positions span Warsaw, Mazowieckie, Poland, Issy-les-Moulineaux, Île-de-France, France, Atlanta, GA, US. Compensation range: $85K - $400K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 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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