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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:
We will consider qualified applicants of all ages who are starting (or restarting) their careers
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-four days per week.
Relocation and housing are not available for this position.
Boston Scientific will not offer sponsorship or take over sponsorship of an employment VISA for this position at this time
Data Scientist – Data & AI Commercial Data Products
We are seeking a passionate and motivated Data Scientist to support our Data & AI Commercial Data Products Team. This role will apply statistical analysis, machine learning, advanced analytics, and artificial intelligence to solve complex business problems, generate actionable insights, and develop data-driven solutions that improve Commercial decision-making and operational effectiveness.
The Data Scientist will work closely with business partners, data engineers, AI/ML teams, and other cross-functional stakeholders to identify high-value opportunities and translate business challenges into analytical solutions. The role will be responsible for exploring and analyzing complex datasets, developing predictive and statistical models, evaluating model performance, and communicating findings and recommendations to technical and non-technical audiences.
In addition to traditional data science capabilities, this role will contribute to the development of generative AI and intelligent agent solutions using technologies such as Snowflake Cortex, large language models (LLMs), and Model Context Protocol (MCP). The Data Scientist will help evaluate where these emerging technologies can complement traditional analytics and machine learning approaches to create scalable, secure, and responsible solutions.
Job Responsibilities* Partner with Commercial business teams and cross-functional stakeholders to identify and prioritize opportunities where data science, advanced analytics, machine learning, and AI can address business challenges and improve decision-making.
- Translate business problems into well-defined analytical questions, hypotheses, data requirements, and measurable success criteria.
- Design, develop, and evaluate predictive and machine learning models for use cases such as forecasting, segmentation, classification, propensity modeling, optimization, and other Commercial applications.
- Perform feature engineering, feature selection, model training, validation, tuning, and performance evaluation to develop accurate, interpretable, and reliable models.
- Develop reusable analytical workflows using Python, SQL, and Snowflake to support data exploration, statistical analysis, machine learning, and AI applications.
- Collaborate with data engineers to identify data requirements, prepare analytical datasets, and ensure data used for modeling and analysis is accurate, reliable, appropriately governed, and fit for purpose.
- Develop and prototype generative AI solutions using Snowflake Cortex and large language models, including capabilities such as summarization, classification, information extraction, embeddings, semantic search, and natural language interaction with enterprise data.
- Implement and evaluate Model Context Protocol (MCP) and related integration approaches to securely provide enterprise data and contextual information to LLM and agent-based solutions.
Required Qualifications
- Must graduate with your bachelor’s degree majoring in Computer Science in December 2026
- Must be eligible to work in the U.S. without company sponsorship, now or in the future, for employment-based work authorization
- Must have prior software engineering internship experience from Boston Scientific
- Must have minimum of 0-1 year of work experience in MedTech
- Must have minimum of 1+ year of work experience in software development
Requisition ID: 636277
Minimum Salary: $ 59900
Maximum Salary: $ 113800
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 $59K-$113K range is in the lower quartile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% 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 $190,000 based on 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($86K) sits 54% below the category median. Disclosed range: $59K to $113K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Boston Scientific AI Hiring
Boston Scientific has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Data Engineer. Based in Arden Hills, MN, US. Compensation range: $113K - $169K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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