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About This Role
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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 healthcare industry is evolving rapidly, creating new opportunities to leverage predictive analytics, AI, and scalable machine learning solutions to improve commercial effectiveness and patient engagement. Boston Scientific is seeking a Data Scientist, Manager, to serve as a technical leader within the Marketing Analytics and Data Science organization, partnering closely with business units, marketing, IT, and data engineering teams to transform complex healthcare and commercial data into scalable, production\-ready AI solutions.
The ideal candidate combines deep expertise in machine learning, forecasting, AI productization, and MLOps with strong business acumen and communication skills. This individual will help shape Boston Scientific’s enterprise AI and predictive analytics capabilities by building scalable, reusable, and activation\-ready solutions that accelerate data\-driven decision\-making across the organization.
The role will report to the Associate Director, Marketing Analytics \& Data Science.
Work Mode:
At Boston Scientific, we value collaboration and synergy. This role follows hybrid work model, requiring employees to be in our local office three days per week.
Relocation:
Relocation assistance is not available for this position at this time.
Your responsibilities will include:
Predictive Analytics \& Forecasting Leadership
- Define and lead the predictive analytics and forecasting strategy, including the development, deployment, optimization, and scaling of advanced models that support Commercial Strategy, Customer Engagement, Market Expansion, and Strategic Business Planning.
- Architect and operationalize machine learning solutions including Propensity, Customer Churn, Lead Scoring, Customer Lifetime Value, Account/Physician Segmentation, and Forecasting models across multiple business units, franchises and products.
- Develop scalable forecasting and predictive intelligence frameworks leveraging Commercial, Claims, Media, CRM, and external datasets to improve business planning and decision\-making.
AI Product Development \& GenAI Enablement
- Lead the vision, design, and delivery of AI\-enabled products and self\-service analytics platforms that improve insight accessibility, workflow efficiency, and enterprise decision\-making.
- Develop and operationalize GenAI and LLM\-powered applications, including conversational analytics interfaces, automated insight generation, metadata enrichment, intelligent recommendation systems, and AI\-assisted decision support tools.
- Establish enterprise AI product frameworks, governance standards, and scalable operating models to accelerate AI adoption, ensure responsible AI usage, and support long\-term platform scalability.
MLOps \& Scalable AI Infrastructure
- Design and implement scalable MLOps frameworks and AI infrastructure to automate model training, validation, deployment, monitoring, retraining, and lifecycle management processes.
- Partner closely with Data Engineering, BU analytics, and IT teams to improve AI/ML deployment efficiency, reduce operational cycle time, and establish scalable and repeatable delivery processes.
- Define and champion best practices for model governance, reproducibility, monitoring, version control, documentation, and AI lifecycle management to improve scalability, reliability, and enterprise AI maturity.
Advanced Analytics \& Decision Science
- Apply advanced statistical, Machine Learning, forecasting, optimization, and experimentation methodologies to solve complex commercial and healthcare business challenges.
- Integrate predictive intelligence into marketing, GTM activation, personalization, experimentation, and strategic business planning processes.
- Evaluate model performance, Business Impact, and operational effectiveness through experimentation, validation frameworks, KPI tracking, and Continuous Optimization.
Cross\-Functional Collaboration \& Strategic Influence
- Partner with Business units, Commercial teams, Marketing, BU Analytics, IT, and executive stakeholders to identify high\-impact opportunities and translate business needs into scalable AI and analytics solutions.
- Serve as a strategic thought partner by translating complex analytical findings into actionable business recommendations and executive\-level insights that influence strategic decisions and investment priorities.
- Influence enterprise analytics and AI roadmap discussions through technical leadership, organizational alignment, and business impact storytelling.
- Lead complex, ambiguous, and cross\-functional initiatives by aligning stakeholders, prioritizing high\-value opportunities, and balancing technical feasibility with business impact.
Technical Leadership \& Organizational Capability Building
- Serve as a recognized subject matter expert in predictive analytics, forecasting, AI productization, GenAI, and MLOps.
- Provide technical leadership and mentorship across the data science organization by establishing modeling standards, scalable development practices, reusable frameworks, and knowledge\-sharing initiatives that elevate overall team capability.
- Drive the evolution of analytics from ad hoc solutions toward scalable, self\-service, and productized AI capabilities embedded within commercial and business workflows.
- Lead and coordinate cross\-functional project teams and provide guidance to analysts, data scientists, and external partners to ensure successful execution of strategic AI and analytics initiatives.
Required Qualifications:
- Minimum of 4\+ years of experience in data science, machine learning, predictive analytics, AI product development, or advanced analytics roles with increasing technical and strategic responsibility.
- Master’s degree or PhD in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, Economics, or a related quantitative field.
- Deep expertise in machine learning, predictive modeling, forecasting, statistical analysis, and scalable AI/ML solution development.
- Advanced proficiency in Python and SQL, with experience working in cloud\-based analytics and machine learning environments.
- Proven experience developing, deploying, and operationalizing production\-grade machine learning solutions and scalable MLOps pipelines.
- Demonstrated ability to lead complex, ambiguous, and cross\-functional analytics initiatives that drive measurable business impact and influence strategic decision\-making.
- Exceptional communication and executive storytelling skills, with the ability to translate complex analytical concepts into clear, actionable business insights that influence strategic decisions, stakeholder alignment, and enterprise adoption.
Preferred Qualifications:
- Experience developing and scaling AI\-enabled products, GenAI applications, and LLM\-powered solutions.
- Experience with MLOps and cloud technologies such as Databricks, Snowflake, MLflow, Airflow, Docker, Kubernetes, GitLab, or related platforms.
- Familiarity with experimentation, causal inference, marketing analytics, media measurement, MMM, and MTA methodologies.
- Experience building scalable AI/ML frameworks, reusable analytics solutions, or enterprise AI capabilities.
- Strong collaboration and stakeholder management skills with the ability to influence cross\-functional teams and align technical solutions with business priorities.
- Demonstrated success leading cross\-functional initiatives and driving projects from concept through operationalization.
- Strong attention to detail with a focus on analytical rigor, solution quality, governance, and operational excellence.
- MBA or additional advanced business or technical certifications preferred.
Requisition ID: 632117
Minimum Salary: $ 89200
Maximum Salary: $ 169500
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 $89K-$169K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Boston Scientific, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($129K) sits 33% below the category median. Disclosed range: $89K to $169K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Boston Scientific AI Hiring
Boston Scientific has 3 open AI roles right now. They're hiring across Data Scientist, Research Engineer, AI/ML Engineer. Positions span Marlboro, MA, US, Santa Clarita, CA, US, Arden Hills, MN, US. Compensation range: $156K - $202K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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