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About This Role
Additional Location(s): US\-MN\-Minneapolis; US\-MN\-Arden Hills; US\-MN\-Maple Grove
Diversity \- Innovation \- Caring \- Global Collaboration \- Winning Spirit \- High Performance
At Boston Scientific, we’ll give you the opportunity to harness all that’s within you by working in teams of diverse and high\-performing employees, tackling some of the most important health industry challenges. With access to the latest tools, information and training, we’ll help you in advancing your skills and career. Here, you’ll be supported in progressing – whatever your ambitions.
About the role:
The Senior Manager, Commercial AI \& Digital Strategy is responsible for defining and leading a new strategic capability within Commercial Operations for the Interventional Oncology \& Embolization division.
This highly visible leadership role will shape how the organization leverages data, artificial intelligence (AI), digital capabilities and commercial insights to accelerate revenue growth, improve sales productivity and enable better business decisions.
This leader will own the commercial strategy behind technology, establishing the vision, roadmap, governance and prioritization for commercial AI while ensuring digital investments deliver measurable business value.
Reporting to the Director of Commercial Operations, this individual will lead a team responsible for commercial intelligence, analytics, AI enablement and digital strategy while partnering closely with Sales, Marketing, Finance, IT, Commercial Excellence and Enterprise AI teams across Boston Scientific.
The ideal candidate is a commercial business leader and technology enabler who is energized by building new capabilities, leading through influence and solving meaningful business problems. This individual naturally connects strategy with execution and is equally comfortable partnering with executives, coaching a team and collaborating with technical experts.
The successful candidate builds trust quickly, communicates with clarity and fosters an environment where accountability, curiosity, collaboration and continuous improvement are expected. This leader is passionate about developing people and building a high\-trust team that becomes a strategic advantage for the commercial organization.
Boston Scientific was recognized as a Glassdoor Best Place to Work in 2026, ranking No. 15 on the Top 100 list, reflecting the culture our employees experience every day.
Work model, sponsorship, relocation:
At Boston Scientific, we value collaboration and synergy. This role follows a hybrid work model requiring employees to be in our local office at least three days per week. Boston Scientific will not offer sponsorship or take over sponsorship of an employment visa for this position at this time. Relocation assistance is not available for this position at this time.
Your responsibilities will include:
Commercial AI and digital strategy
- Define, lead and evolve the commercial AI and digital strategy aligned with business priorities.
- Own the long\-term vision, roadmap, governance and prioritization of commercial AI, digital strategy and commercial intelligence.
- Identify opportunities where AI, automation, analytics and digital capabilities can improve revenue growth, sales productivity, forecasting, customer engagement and commercial execution.
- Evaluate emerging technologies and prioritize investments based on business value and adoption.
- Establish key performance indicators and governance to measure business outcomes.
- Represent Commercial Operations on enterprise AI and digital transformation initiatives.
- Champion innovation while balancing governance, scalability and business value.
Commercial intelligence and business insights
- Build a commercial intelligence capability that transforms data into actionable decisions.
- Lead reporting, dashboards, analytics and business intelligence strategy, partnering with leaders to translate business questions into actionable insights.
- Advance predictive and prescriptive analytics capabilities.
- Champion data quality and governance.
Digital product strategy and business enablement
- Translate commercial strategy into scalable digital capabilities.
- Define business requirements, success metrics and adoption strategies.
- Lead pilots and scale successful solutions.
- Drive change management and user adoption.
- Simplify commercial workflows through automation and AI.
People leadership and organizational development
- Coach and develop a high\-performing team.
- Create a culture of accountability, collaboration, innovation and continuous improvement.
- Lead through ambiguity by establishing clear priorities, roles, responsibilities and momentum across the team.
- Lead organizational change and adoption of new capabilities.
Required qualifications:
- Minimum of 5 years' experience in Commercial Operations, Commercial Excellence, Sales Operations, Business Strategy, Management Consulting, Digital Transformation, Commercial Analytics or related business functions.
- People leadership experience.
- Experience defining and executing commercial strategy, AI roadmaps or digital strategies in a matrixed organization.
- Ability to translate business challenges into AI\-enabled, analytical or digital solutions.
- Executive communication and influencing skills.
- Familiarity with Salesforce, Tableau, Power BI, Snowflake, Alteryx or similar platforms.
- Strong business acumen and change leadership.
Preferred qualifications:
- Medical device, health care or life sciences experience.
Requisition ID: 633236
Minimum Salary: $ 125800
Maximum Salary: $ 239000
The anticipated compensation listed above and the value of core and optional employee benefits offered by Boston Scientific (BSC) – see www.bscbenefitsconnect.com—will vary based on actual location of the position and other pertinent factors considered in determining actual compensation for the role. Compensation will be commensurate with demonstrable level of experience and training, pertinent education including licensure and certifications, among other relevant business or organizational needs. At BSC, it is not typical for an individual to be hired near the bottom or top of the anticipated salary range listed above.
Compensation for non\-exempt (hourly), non\-sales roles may also include variable compensation from time to time (e.g., any overtime and shift differential) and annual bonus target (subject to plan eligibility and other requirements).
Compensation for exempt, non\-sales roles may also include variable compensation, i.e., annual bonus target and long\-term incentives (subject to plan eligibility and other requirements).
For MA positions: It is unlawful to require or administer a lie detector test for employment. Violators are subject to criminal penalties and civil liability.
Boston Scientific transforms lives through innovative medical technologies that improve the health of patients around the world. As a global medical technology leader for more than 45 years, we advance science for life by providing a broad range of high\-performance solutions that address unmet patient needs and reduce the cost of healthcare. Our portfolio of devices and therapies helps physicians diagnose and treat complex cardiovascular, respiratory, digestive, oncological, neurological and urological diseases and conditions. Learn more at www.bostonscientific.com and follow us on LinkedIn.
Boston Scientific Corporation has been and will continue to be an equal opportunity employer. To ensure full implementation of its equal employment policy, the Company will continue to take steps to assure that recruitment, hiring, assignment, promotion, compensation, and all other personnel decisions are made and administered without regard to race, religion, color, national origin, citizenship, sex, sexual orientation, gender identity, gender expression, veteran status, age, mental or physical disability, genetic information or any other protected class.
Please be advised that certain US based positions, including without limitation field sales and service positions that call on hospitals and/or health care centers, require acceptable proof of COVID\-19 vaccination status. Candidates will be notified during the interview and selection process if the role(s) for which they have applied require proof of vaccination as a condition of employment. Boston Scientific continues to evaluate its policies and protocols regarding the COVID\-19 vaccine and will comply with all applicable state and federal law and healthcare credentialing requirements. As employees of the Company, you will be expected to meet the ongoing requirements for your roles, including any new requirements, should the Company’s policies or protocols change with regard to COVID\-19 vaccination.
Among other requirements, Boston Scientific maintains specific prohibited substance test requirements for safety\-sensitive positions. This role is deemed safety\-sensitive and, as such, candidates will be subject to a prohibited substance test as a requirement. The goal of the prohibited substance testing is to increase workplace safety in compliance with the applicable law.
Salary Context
This $125K-$239K range is above the median 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 Boston 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($182K) sits 15% below the category median. Disclosed range: $125K to $239K.
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.
Boston Scientific AI Hiring
Boston Scientific has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Arden Hills, MN, US, Maple Grove, MN, US. Compensation range: $156K - $239K.
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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