Principal Data Scientist - Marketing Analytics

Additional Location(s): US-MN-Maple Grove; US-MN-Arden Hills

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:

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.

 

Boston Scientific is seeking an experienced Principal Data Scientist – Marketing Analytics to join our Cardiology Marketing and Digital Enablement team. This role will apply advanced analytics, statistical modeling, and machine learning techniques to solve business problems, develop marketing effectiveness models, and generate actionable insights.The Data Scientist will partner closely with cross-functional stakeholders to translate business needs into scalable analytical solutions and will support models across the full lifecycle, from problem framing through deployment, monitoring, and continuous improvement.

 

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 Arden Hills or Maple Grove 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: 

• Partner with marketing and business stakeholders to define analytical problems, success criteria, data requirements, and measurable business outcomes.
• Develop machine learning and statistical models, including approaches such as regression, classification, clustering, attribution modeling, marketing mix modeling, lead scoring, causal inference, and controlled marketing experiments.
• Support the full model lifecycle, including feature engineering, model validation, documentation, deployment support, performance monitoring, and ongoing refinement.
• Communicate insights, model outputs, and recommendations clearly to both technical and non-technical stakeholders.
• 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.  

 

Required qualifications: 

• Master’s degree in data science, Statistics, Economics, Computer Science, Engineering, or a related field.
• 8+ years of experience in data science, clinical analytics, health care analytics or a related analytical role.
• Advanced proficiency in Python and SQL, with experience working in cloud-based analytics and machine learning environments. 
• Experience developing statistical or machine learning models using Python and related frameworks, such as scikit-learn, statsmodels, XGBoost, PyMC-Marketing, or Google Meridian.
• Strong analytical thinking and ability to translate business problems into data-driven analytical solutions and communicate technical concepts and findings clearly to business stakeholders.
• Demonstrated ability to collaborate across business and technical teams.

 

Preferred qualifications:

• Experience with Snowflake or similar analytics platforms.
• Experience working with health care, medical device or other highly regulated data environments.
• Experience working with health insurance claims data, patient diagnosis data, physician notes and prescription data.
• Familiarity with MLOps and model