Interested in this AI/ML Engineer role at Information Technology Senior Management Forum?
Apply Now →Skills & Technologies
About This Role
Posted Date
5/27/2026
Description
#### We are the people who give possibilities purpose
BD is one of the largest global medical technology companies in the world. Advancing the world of health™ is our Purpose, and it’s no small feat. It takes the imagination and passion of all of us—from design and engineering to the manufacturing and marketing of our billions of MedTech products per year—to look at the impossible and find transformative solutions that turn dreams into possibilities.
#### Job Description
We are the makers of possible!
BD is one of the largest global medical technology companies in the world. Advancing the world of health™ is our Purpose, and it’s no small feat. It takes the imagination and passion of all of us—from design and engineering to the manufacturing and marketing of our billions of MedTech products per year—to look at the impossible and find transformative solutions that turn dreams into possibilities.
We believe that the human element, across our global teams, is what allows us to continually evolve. Join us and discover an environment in which you’ll be supported to learn, grow and become your best self. Become a maker of possible with us.
We are seeking a strategic, data\-savvy, and AI\-forward leader to serve as Commercial Director, Data \& AI Product Management — responsible for shaping the commercial data, insights, and AI product strategy across the full commercial ecosystem, from marketing and demand generation through pricing, sales operations, and CRM to service, repairs, and installed\-base management.
This role will define and own the product vision, roadmap, and lifecycle for commercial data products, insights platforms, and AI/GenAI\-powered solutions. Critically, this role partners with a shared Data, Insights \& AI product delivery organization to build, deploy, and manage these products — meaning this role is the strategic product management authority (the “what” and “why”) while partnering with the delivery organization on the “how” and “when.”
This person is a peer to the three Senior leaders and collaborates closely with all three to ensure data, insights, and AI capabilities are embedded across every layer of the commercial technology ecosystem:
- Director, Global CRM, Sales Operations \& Revenue Technology— embedding data, insights, and AI into CRM, CPQ, CLM, Sales Planning, and ICM platforms
- Associate Director, Enterprise Architecture — ensuring data products and AI capabilities align with the target\-state commercial architecture, integration patterns, and data governance standards
- Senior Director, Marketing, Service \& Repairs Technology — embedding insights and AI into MarTech, FSM, repairs, and installed\-base platforms
This leader will bring deep product management expertise in data, analytics, and AI — combined with strong commercial acumen and the ability to translate complex data and AI capabilities into measurable business outcomes that drive revenue growth, operational efficiency, and customer experience improvement across a global MedTech organization.
#### Responsibilities:
#### Commercial Data Strategy \& Product Vision
- Define the multi\-year commercial data product strategy and roadmap spanning marketing, sales, service, and repairs data domains.
- Own the product vision for commercial data assets — including curated data products, master data services, customer 360 views, product/installed\-base data, and commercial performance datasets.
- Define data product standards (SLAs, quality metrics, discoverability, self\-service) in partnership with Enterprise Architecture.
- Champion a product\-based data operating model — treating data as a managed product with clear ownership, consumers, quality SLAs, and lifecycle management.
#### Insights Products \& Commercial Analytics
- Define and own the product roadmap for commercial insights platforms — AI enabled dashboards, self\-service analytics, embedded analytics, and decision\-support tools across sales, marketing, and service.
- Translate business needs into insights product requirements — including sales performance analytics, pipeline/forecast insights, marketing attribution, service KPIs, pricing analytics, and installed\-base intelligence.
- Define KPIs and success metrics for insights products — measuring adoption, business impact, and decision\-making improvement.
- Partner with Commercial, Finance, and Business Unit leadership to prioritize insights investments aligned to strategic priorities.
#### AI \& GenAI Product Management
- Define and own the AI/GenAI product strategy for the commercial ecosystem — including intelligent pricing, guided selling, predictive lead scoring, AI\-assisted service triage, generative AI for content/proposals, agentic AI for workflow automation, and conversational AI.
- Own the full AI product lifecycle — from opportunity identification and business case development through MVP, pilot, scale, and ongoing optimization.
- Establish AI product governance frameworks — including responsible AI principles, bias monitoring, explainability standards, human\-in\-the\-loop requirements, and compliance with healthcare/MedTech regulations.
- Horizon\-scan emerging AI technologies (LLMs, multi\-agent systems, autonomous agents) and evaluate commercial applicability.
- Define and track AI product value realization metrics — measuring ROI, adoption, productivity gains, and commercial impact.
#### Partnership with Shared Delivery Organization
- Serve as the primary product management partner to the shared Data, Insights \& AI product delivery organization — providing clear product vision, prioritized backlogs, acceptance criteria, and business context.
- Operate as the strategic “demand side” — articulating the “what” and “why” while the delivery organization owns the “how” and “when.”
- Establish joint operating rhythms (sprint reviews, roadmap planning, quarterly business reviews) with the delivery organization to ensure alignment, velocity, and value delivery.
- Co\-own product lifecycle governance — including intake, prioritization, release planning, and retirement of data, insights, and AI products.
#### Cross\-Ecosystem Collaboration
- Partner with Director, Global CRM, Sales Operations \& Revenue Technology to embed data, insights, and AI into CRM, CPQ, CLM, ICM, and Sales Planning platforms.
- Partner with Sr. Director, Marketing, Service \& Repairs Technology to embed insights and AI into MarTech, FSM, repairs, and installed\-base platforms.
- Partner with Associate Director, Enterprise Architecture to ensure data products, insights, and AI capabilities align with the target\-state commercial architecture, integration patterns, and data governance standards.
- Serve as the commercial voice to enterprise\-wide Data \& AI governance forums.
#### Stakeholder Leadership \& Change Management
- Serve as the executive\-facing owner of the commercial data, insights, and AI product portfolio — presenting strategy, progress, and value realization to senior leadership.
- Drive data literacy, AI fluency, and insights adoption across commercial teams — fostering a culture of data\-driven decision\-making.
- Lead change management for new AI capabilities — ensuring trust, adoption, and responsible use across the organization.
Required Qualifications:
- 12\+ years of progressive experience in data product management, analytics product management, AI/ML product management, or data strategy — with at least 4 years in a leadership role.
- Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, Business, or related field; advanced degree (MBA, MS, PhD) preferred.
- Demonstrated experience defining and managing data products, insights platforms, and/or AI products in large, complex organizations.
- Strong understanding of modern data architectures — data lakes/lakehouses, data mesh, data products, CDPs, and analytics platforms (e.g., Snowflake, Databricks, Azure Synapse, Power BI, Tableau).
- Experience with AI/ML product management — including understanding of LLMs, GenAI, NLP, predictive models, and responsible AI frameworks.
- Proven ability to operate in a product management capacity separate from delivery — defining strategy, vision, and priorities while partnering with engineering/delivery teams on execution.
- Experience with Agile product management methodologies and operating as a strategic Product Owner.
- Strong understanding of commercial operations — including sales, marketing, pricing, service, and CRM processes.
- Medical device, MedTech, life sciences, or regulated industry experience required.
- Experience in leading and applying lean and kaizen excellence principles and techniques such as PSP and 5\-Whys in achieving commercial objectives of driving growth, productivity, margin expansion and improved customer engagement.
#### Preferred Qualifications
- Experience with agentic AI, multi\-agent orchestration, and autonomous AI systems in commercial contexts.
- Familiarity with data governance frameworks (DMBOK, DCAM) and data quality management practices.
- Experience with customer data platforms (CDPs) and identity resolution in B2B/B2B2C environments.
- Knowledge of responsible AI and ethics frameworks — including bias detection, explainability (XAI), and compliance with healthcare\-specific AI regulations.
- Experience building data literacy and AI fluency programs at enterprise scale.
- Familiarity with MLOps, model monitoring, and AI product observability practices.
What We Offer
- The opportunity to define and shape the commercial data, insights, and AI product strategy for a global MedTech leader.
- A high\-visibility role at the intersection of data, AI, and commercial strategy — with direct influence on how the organization leverages intelligence to compete and grow.
- Partnership with three peer Senior Directors and exposure to C\-suite and senior commercial leadership.
- A culture that values innovation, data\-driven decision\-making, and responsible AI.
- Competitive compensation, including base salary, annual bonus, equity (if applicable), and comprehensive benefits.
Why Join Us?
A career at BD means being part of a team that values your opinions and contributions and that encourages you to bring your authentic self to work. It’s also a place where we help each other be great, we do what’s right, we hold each other accountable and learn and improve every day.
You will learn and work alongside inspirational leaders and colleagues who are equally passionate and committed to fostering an inclusive, growth\-centered, and rewarding culture. You will have the opportunity to help shape the trajectory of BD while leaving a legacy at the same time. And through the organization’s investment in BD University, you will continually level up your tech skills and expertise.
To find purpose in the possibilities, we need people who can see the bigger picture, who understand the human story that underpins everything we do. We welcome people with the imagination and drive to help us reinvent the future of health. At BD, you’ll discover a culture in which you can learn, grow and thrive. And find satisfaction in doing your part to make the world a better place.
To learn more about BD visit https://bd.com/careers
Becton, Dickinson and Company is an Equal Opportunity Employer. We evaluate applicants without regard to race, color, religion, age, sex, creed, national origin, ancestry, citizenship status, marital or domestic or civil union status, familial status, affectional or sexual orientation, gender identity or expression, genetics, disability, military eligibility or veteran status, and other legally\-protected characteristics.
#### Why Join Us?
To find purpose in the possibilities, we need people who can see the bigger picture, who understand the human story that underpins everything we do. We welcome people with the imagination and drive to help us reinvent the future of healthcare. At BD, you’ll discover a culture in which you can learn, grow and thrive.
We believe that when people connect in person, we learn faster, collaborate more deeply, and build a stronger culture. Join us and enjoy a culture where face\-to\-face collaboration supports your learning, your progress, and your success.
To learn more about BD visit https://bd.com/careers.
Becton, Dickinson, and Company is an Equal Opportunity Employer. We evaluate applicants without regard to race, color, religion, age, sex, creed, national origin, ancestry, citizenship status, marital or domestic or civil union status, familial status, affectional or sexual orientation, gender identity or expression, genetics, disability, military eligibility or veteran status, and other legally protected characteristics.
Required Skills
Optional Skills
.
#### Primary Work Location
USA NJ \- Franklin Lakes#### Additional Locations
#### Work Shift
At BD, we reward, support and develop our associates through our comprehensive Total Rewards program. We are committed to attracting and retaining high quality talent by providing reward and recognition opportunities that promote a performance\-based culture, as well as a competitive package of compensation and benefits programs. You can learn more on our career site under "Our Commitment to You."
Our salary or hourly rate ranges reward associates fairly and competitively. We regularly review these ranges and factors, such as location, contribute to the range displayed.
Our pay is based on the role and the necessary skills and education to perform it successfully. The salary or hourly rate offered is determined by the role's specific requirements, including any applicable step rate pay system at the work location. Salary or hourly pay ranges are influenced by labor laws and Collective Bargaining Agreement (CBA) requirements applicable to the work location which may also affect the workplace arrangement of the role.
Salary Range Information
Type
Full\-time
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Information Technology Senior Management Forum, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 34 open AI roles right now. They're hiring across AI Engineering Manager, Data Scientist, AI/ML Engineer, Data Engineer. Positions span San Jose, CA, US, Jersey City, NJ, US, McLean, VA, US. Compensation range: $167K - $335K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.