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We are the people who give possibilities purpose
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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
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Key Responsibilities
Global Commercial Data \& AI Strategy
- Own the enterprise commercial data, analytics, AI, and decision intelligence strategy.
- Define the long\-term roadmap for how commercial data, advanced analytics, AI, and governance capabilities evolve to support business growth.
- Establish enterprise priorities and investment strategies for commercial intelligence capabilities.
- Ensure alignment between commercial business strategy, data strategy, AI strategy, and commercial technology roadmaps.
- Partner with Commercial Data Products and Commercial Data Analytics Engineering to define decision science use cases, business requirements, and operationalize analytics models.
AI \& Advanced Analytics Leadership
- Lead the development and deployment of advanced analytical solutions across sales, forecasting, market development, customer success, and commercial operations.
- Build and scale machine learning, predictive analytics, optimization, simulation, experimentation, and recommendation engines.
- Drive adoption of modern AI capabilities including generative AI, agentic AI, copilots, reasoning systems, knowledge discovery, and intelligent workflow automation.
- Maintaindeepunderstanding of advancements in AI, economic conditions, market trends, and competitive forces shaping the future of commercial execution.
- Assess emerging technologies including agentic AI, reasoning systems, simulation platforms, digital commercial assistants, and real\-time decision intelligence capabilities.
Data Governance \& Trust
- Establish governance\-by\-design principles that embed trust, quality, compliance, and stewardship into workflows rather than relying on manual processes.
- Define enterprise standards for metadata management, lineage, cataloging, master data, data quality, retention, access management, and business semantics.
- Establish and govern enterprise KPI definitions, metric hierarchies, commercial data standards, and semantic consistency across Business Units and Regions.
- Lead governance councils and stewardship forums supporting commercial data and AI.
- Serve as the ultimate point of accountability for commercial data trust, adoption, transparency, quality, and usability.
Commercial Insights \& Strategic Growth
- Establish enterprise frameworks for predictive, prescriptive, and scenario\-based decision making.
- Develop analytical assets thatidentifygrowth opportunities, revenue risks, whitespace opportunities, customer trends, territory optimization opportunities, and sales execution gaps.
- Drive executive\-level business reviews using forward\-looking insights and recommendations.
- Ensure analytical outputs are translated into actionable business decisions.
- Serve as a strategic advisor to executive leadershipregardingcommercial performance, market dynamics, growth opportunities, and resource allocation.
- Establish value realization frameworks that measure adoption, business impact, and return on investment from commercial analytics, AI, and decision intelligence initiatives.
- Lead the evolution from descriptive reporting to predictive and prescriptive decision intelligence capabilities that improve forecasting, resource allocation, customer engagement, and commercial productivity.
Responsible AI\&Compliance Controls
- Own the commercial AI governance framework, including model risk management, AI policies, controls, and standards.
- Establish responsible AI principles covering explainability, bias mitigation, transparency, human oversight, and model monitoring.
- Partner with Legal, Privacy, Compliance, Cybersecurity, and IT to ensure AI and data usage meets evolving regulatory requirements.
- Define governance approaches for GenAI, LLMs, agentic systems, autonomous workflows, and future AI technologies.
- Adviseexecutive leadership on AI readiness, risk exposure, maturity, and investment decisions.
Talent \& Organizational Leadership
- Build and lead a world\-class team of data scientists, AI specialists, governance leaders, and advanced analytics professionals.
- Foster a culture of agility, learning, innovation, accountability, and business impact.
- Build and scale a multi\-disciplinary organization spanning decision science, AI strategy, governance, business intelligence, and commercial analytics excellence.
- Develop organizational capabilities that keep pace with rapid advances in AI, analytics, governance, and commercial strategy.
- Serve as a mentor, strategist, and thought leader across the global commercial organization.
Qualifications
- 12\+ years of progressive experience in decision science, data science, artificial intelligence, advanced analytics, commercial data strategy, data governance, or related fields.
- 7\+ years of leadership experience leading data science, AI, analytics, governance, data strategy, or decision intelligence teams.
- Proven success applying predictive modeling, machine learning, optimization, experimentation, forecasting, causal analytics, and advanced analytics in commercial environments.
- Deep understanding of commercial data governance, metadata management, lineage, data quality, access controls, privacy, compliance, and enterprise risk management.
- Strong understanding of modern AI technologies including GenAI, LLMs, agentic systems, retrieval\-augmented generation, and intelligent automation.
- Experience translating complex analytical, governance, and AI concepts into executive\-level recommendations and business actions.
- Strong business acumen and understanding of commercial operations, revenue growth, sales effectiveness, customer engagement, and strategic planning.
- Experienceoperatingwithin global, matrixed organizations across multiple Business Units, Regions, and regulatory environments.
Education:Advanced degree in Data Science, Statistics, Mathematics, Engineering, Computer Science, Business Analytics, Information Systems, or relatedfieldrequired.
Desired Skills \& Experience
- Experience within MedTech, Life Sciences, Healthcare, or similarly regulated industries preferred.
- Expertisein applied machine learning, statistical modeling, optimization, experimentation, decision science, and causal analytics.
- Experience implementing governance\-by\-design approaches that embed controls into platforms, workflows, and product development processes.
- Experience building enterprise AI products and deploying models into production environments.
- Understanding ofmodern cloud analytics ecosystems, data product operating models, semantic layers, MDM, metadata management, and data quality tooling.
- Experienceestablishinggovernance councils, stewardship forums, decision rights, and executive reporting mechanisms for enterprise data and AI capabilities.
- Ability to influence senior leaders through evidence\-based recommendations, practical governance models, and clear value\-realization frameworks.
Leadership Attributes
- Strategic Futurist:Anticipateshow AI, market dynamics, competitive shifts, regulation, and technology innovation will reshape commercial execution.
- Decision Intelligence Leader:Focuses on improving the quality and speed of decisions, not just generating insights.
- Enterprise Collaborator:Builds strong partnerships across business, technology, risk, and operational functions.
- Outcome Driven:Relentlessly focused on adoption, value realization, revenue impact, and business results.
- Executive Influencer:Communicates complex concepts with clarity and confidence while shaping strategic direction.
Why Join Us?
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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
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Primary Work Location
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USA IL \- Vernon HillsAdditional Locations
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Work Shift
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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
$191,900\.00 \- $307,100\.00 USD Annual
Salary Context
This $191K-$307K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BD, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($249K) sits 16% above the category median. Disclosed range: $191K to $307K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BD AI Hiring
BD has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Vernon Hills, IL, US, Laguna Canyon, CA, US. Compensation range: $207K - $307K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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