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About This Role
Work Schedule
Standard (Mon-Fri)Environmental Conditions
OfficeJob Description
As part of the Thermo Fisher Scientific team, you’ll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner and safer. We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world’s toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer.
Thermo Fisher Scientific is seeking an experienced and strategic digital product leader to define and drive the commercial technology, data, customer intelligence, and AI capability roadmap that will enable the next generation of our commercial experience.
The Director, Digital Product Management – Commercial AI & Intelligence will own the product strategy and integrated capability roadmap across Commercial AI, Sales technology, Marketing technology, the Commercial Insights Platform, Salesforce-enabled commercial experiences, and the data capabilities required to enable them. This leader will connect commercial strategy with product, technology, data, and architecture requirements, partnering closely with businesses, Sales, Marketing, IT, Data & Analytics, Enterprise Architecture, and strategic external partners to ensure investments build toward a common enterprise vision.
This is not a traditional application, platform, or technology management role. The Director will establish the North Star for how technology, data, customer intelligence, and AI come together to transform how our commercial teams work. The role will define how commercial colleagues understand customers, identify opportunities, generate insights, determine next-best actions, collaborate across the enterprise, and increasingly execute work through AI-enabled and agentic experiences.
The successful candidate will operate at the intersection of business strategy, digital product management, data, AI, and enterprise technology. This individual must be equally comfortable engaging senior business and technology executives, shaping investment and architecture decisions, and working with product, data, architecture, engineering, and business teams to translate strategy into scalable enterprise capabilities and measurable commercial outcomes.
DESCRIPTION
How you will make an impact:
You will define the future of Thermo Fisher's commercial digital experience by establishing an integrated product strategy connecting Sales technology, Marketing Technology, the Commercial Insights Platform, enterprise data, analytics, AI, and emerging agentic capabilities.
You will ensure technology investments are driven by commercial strategy and build toward scalable enterprise capabilities rather than disconnected solutions. Working across businesses and functions, you will translate advances in AI and technology into practical capabilities that improve commercial productivity, customer engagement, opportunity identification, decision-making, and revenue growth.
What you will do:
Commercial AI & Digital Product Strategy
- Establish and communicate the Commercial AI & Digital Product North Star, defining how Sales technology, Marketing technology, commercial insights, data, analytics, AI, and digital capabilities come together to enable the future commercial organization.
- Own the multi-year integrated product strategy and capability roadmap, translating business priorities and emerging technologies into clear priorities, sequencing, dependencies, and investment requirements.
- Establish enterprise product principles and decision frameworks that balance near-term business value with scalability, reuse, governance, interoperability, and long-term strategic value.
Lead Commercial AI Product Strategy
- Define the product strategy for applying generative AI, predictive intelligence, and agentic capabilities across Sales and Commercial workflows.
- Translate advancements in AI into practical capabilities that improve productivity, generate insights, identify opportunities, recommend next-best actions, and increasingly automate commercial work.
- Establish a disciplined approach for identifying, testing, measuring, governing, and scaling the highest-value AI use cases across Thermo Fisher.
Define the Future Commercial Experience
- Develop the product vision for how AI, commercial insights, customer and commercial data, Salesforce, collaboration technologies, and analytics come together to create a differentiated commercial experience.
- Partner directly with Sales, Commercial, Marketing, and business teams to understand workflows and needs and translate them into scalable enterprise capabilities.
- Embed intelligence, recommendations, and automation into the tools and workflows commercial colleagues use to prepare for customers, identify opportunities, collaborate, make decisions, and execute actions.
Commercial Insights Platform
- Provide strategic product leadership for the Commercial Insights Platform, establishing it as the enterprise capability for delivering actionable customer, account, opportunity, market, and commercial intelligence.
- Define how internal and external data, digital activity, customer content, competitive intelligence, and analytical insights come together to support account intelligence, deep research, customer preparation, opportunity intelligence, and next-best actions.
- Guide the evolution of the Commercial Insights Platform toward an integrated AI-enabled experience with shared context, reusable intelligence, enterprise data, and integration into Salesforce and other commercial workflows.
Connect Sales, Commercial Insights & Data Strategy
- Define the data strategy required to support Commercial AI, Commercial technology and the Commercial Insights Platform, including customer, account, contact, interaction, opportunity, intent, product, transaction, and content data.
- Partner with IT, Enterprise Data, Data & Analytics, Data Science, and business teams to prioritize data integration, enrichment, quality, identity, metadata, accessibility, and AI-readiness.
- Ensure first-party, third-party, digital, analytical, and commercial data investments are directly connected to prioritized business use cases and measurable commercial outcomes.
Shape Data & Technology Architecture
- Partner with IT, Enterprise Architecture, Data & Analytics, Data Science, and engineering teams to define the technology and data foundations required to deliver the Commercial AI and digital product roadmap.
- Influence architecture, platform, integration, security, and data decisions to ensure solutions are scalable, reusable, interoperable, and aligned to long-term commercial strategy.
- Bring sufficient technical fluency to architecture discussions to challenge assumptions, understand dependencies and tradeoffs, and advocate for enterprise capabilities rather than disconnected point solutions.
Sales Technology & Salesforce Strategy
- Define the strategic product roadmap for enterprise Sales technology in partnership with Sales, Commercial Operations, IT, and business stakeholders.
- Establish the vision for how Salesforce and the broader commercial technology ecosystem enable customer intelligence, opportunity management, workflow automation, collaboration, next-best actions, and AI-enabled selling.
- Identify opportunities to standardize and scale common enterprise Sales capabilities while accommodating business-specific requirements where they create meaningful value.
Strategic Technology Partnerships
- Serve as a key Thermo Fisher product leader across strategic relationships with OpenAI, Salesforce, consulting partners, and other emerging technology providers.
- Translate partner innovation and technology roadmaps into practical implications, opportunities, and capability priorities for Thermo Fisher.
- Use strategic partnerships to accelerate high-value capabilities while informing build, buy, partner, and integration decisions aligned to enterprise product and architecture strategies.
Product Portfolio & Investment Prioritization
- Own prioritization across the Commercial AI & Digital Product portfolio, balancing business value, customer impact, productivity, revenue potential, strategic importance, cost, risk, and dependencies.
- Develop compelling business cases and recommendations for new capabilities and investments and present them to senior Commercial, business, Finance, and technology leaders.
- Establish clear frameworks for determining where Thermo Fisher should invest in common enterprise capabilities versus business-specific solutions.
Enterprise Alignment & Executive Leadership
- Build alignment across Commercial, Marketing, Sales, businesses, IT, Data & Analytics, Enterprise Architecture, Data Science, and other functions around a common Commercial AI and digital product strategy.
- Serve as a trusted advisor to senior leaders, translating complex AI, technology, architecture, and data topics into clear business implications, recommendations, tradeoffs, and decisions.
- Lead executive roadmap, strategy, investment, and prioritization discussions while creating clear visibility into progress, risks, dependencies, adoption, and value realization.
Product Leadership & Team Development
- Build and lead a high-performing digital product management organization responsible for Commercial AI, Commercial technology, the Commercial Insights Platform, and enabling data capabilities.
- Establish clear ownership, accountability, decision rights, and product management standards while coaching product leaders to independently manage complex products and initiatives.
- Develop product management talent capable of operating effectively across business strategy, technology, data, AI, commercial transformation, and executive stakeholder management.
Thought Leadership & External Orientation
- Maintain a strong external perspective on developments in AI, agentic technology, CRM, commercial intelligence, Commercial technology, analytics, and enterprise data platforms.
- Translate emerging technologies and market trends into clear strategic implications, opportunities, and recommendations for Thermo Fisher leadership.
- Identify opportunities to leverage Thermo Fisher's scale, scientific expertise, customer relationships, data, and commercial capabilities to create differentiated AI-enabled experiences.
Measurement & Value Realization
- Define the KPIs and outcome measures used to evaluate Commercial AI & Digital Product investments, including adoption, productivity, customer impact, commercial effectiveness, revenue enablement, and cost savings.
- Ensure business cases include measurable expected outcomes and establish mechanisms to assess post-launch value realization.
- Use performance data, user feedback, and business outcomes to continuously improve capabilities, adjust priorities, and guide future investments.
- Performs other duties as assigned.
REQUIREMENTS
How you will get here:
Education
- Bachelor's degree required in Business, Marketing, Information Systems, Computer Science, Data & Analytics, Engineering, or a related field.
- Master's degree in Business, Technology, Data, Analytics, Engineering, or a related discipline preferred.
Experience
- 10+ years of relevant experience across digital product management, commercial technology, enterprise data and analytics, AI, enterprise platforms, customer experience technology, or technology-enabled transformation.
- Demonstrated experience owning product strategy and multi-year roadmaps for complex enterprise capabilities.
- Experience leading large, cross-functional initiatives across business, product, technology, data, architecture, analytics, and engineering teams within a highly matrixed organization.
- Experience working with enterprise Sales and CRM technologies; Salesforce experience strongly preferred.
- Experience working with enterprise Marketing technologies; Adobe technology experience strongly preferred.
- Experience with customer intelligence, enterprise data platforms, analytical products, AI-enabled capabilities, or commercial transformation strongly preferred.
- Demonstrated experience translating business strategy into product capabilities, architecture requirements, investment priorities, and measurable outcomes.
- Experience building business cases and influencing investment decisions with senior business and technology leaders.
- Experience managing strategic relationships with major technology vendors, consulting organizations, or external innovation partners.
- Demonstrated experience influencing technology, architecture, and data decisions without direct organizational authority.
- Experience leading, coaching, and developing product managers or other professional teams preferred.
Skills and Competencies
- Exceptional digital product management and product strategy capabilities.
- Strong understanding of commercial processes, CRM technologies, Marketing technologies, customer intelligence, and enterprise commercial technology ecosystems.
- Strong understanding of generative AI, machine learning, emerging agentic capabilities, and the practical application of AI within enterprise workflows.
- Working knowledge of enterprise data strategy, customer data, data architecture, analytics, identity, integration, and AI-ready data requirements.
- Sufficient technical fluency to participate meaningfully in architecture discussions, challenge assumptions, understand dependencies, evaluate tradeoffs, and ensure technical decisions align to long-term product strategy.
- Ability to define a compelling future-state vision and translate that vision into an executable multi-year roadmap.
- Strong strategic thinking combined with the ability to move from ambiguous business problems to defined capabilities, priorities, and decisions.
- Exceptional communication, presentation, storytelling, and executive presence.
- Ability to simplify complex technology and data topics into clear business implications and executive recommendations.
- Strong business-case development, financial analysis, prioritization, and investment decision-making skills.
- Demonstrated ability to build consensus and alignment across multiple businesses, functions, regions, and leadership teams.
- Strong influence and stakeholder-management skills, including the ability to drive decisions without direct authority.
- Strong external orientation and demonstrated ability to translate technology trends into practical business opportunities.
- Strong leadership, coaching, organizational development, and talent-management capabilities.
- Ability to operate effectively in a large, global, highly matrixed organization and navigate ambiguity and competing priorities.
- Strong analytical mindset with an emphasis on measurable business outcomes and value realization.
- High standards of integrity, accountability, collaboration, and professional judgment.
Preferred Qualifications
- Product leadership experience with Salesforce, OpenAI technologies, enterprise AI platforms, commercial insights solutions, or related commercial technology ecosystems.
- Experience developing or scaling generative AI and agentic AI capabilities within enterprise Sales, Marketing, or Commercial workflows.
- Experience with customer intelligence, account intelligence, next-best-action, opportunity intelligence, sales recommendations, or AI-enabled customer engagement.
- Familiarity with enterprise data platforms, CRM data, digital behavioral data, intent data, customer data, analytical models, and third-party enrichment data.
- Experience creating integrated product strategies that span multiple platforms and organizational owners.
- Experience working with Enterprise Architecture, Data Engineering, Data Science, Cybersecurity, Privacy, and other technology governance organizations.
- Experience leading commercial or digital transformation in a large global enterprise.
- Strong PowerPoint and executive storytelling skills with demonstrated ability to communicate strategy, architecture, investment recommendations, roadmaps, and business impact to senior leadership.
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 Thermo Fisher Scientific, 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.
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.
Thermo Fisher Scientific AI Hiring
Thermo Fisher Scientific has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pittsburgh, PA, US.
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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