Data Product Manager, IC

Boston Scientific•Maple Grove, MN

4d•$102,100 - $194,000•Hybrid

About The Position

Boston Scientific's Cardiology Strategy & Business Intelligence organization is accelerating its digital transformation and is seeking an experienced Data Product Manager to help shape the future of enterprise data, advanced analytics, and AI-enabled decision making. This role partners cross-functionally to lead the strategy, governance, and evolution of a modern data platform that serves as the trusted foundation for business intelligence, advanced analytics, data science, and emerging AI capabilities across the Cardiology franchise. Working closely with business leaders, data engineers, architects, analysts, and data scientists, you will define scalable data products that transform complex enterprise data into actionable insights and intelligent business solutions. The ideal candidate combines strong business acumen with deep technical knowledge of modern data platforms, cloud technologies, enterprise data modeling, and governance. They understand how high-quality, well-modeled data enables predictive analytics, machine learning, generative AI, and self-service analytics while maintaining the highest standards of security, quality, and compliance. If you're passionate about building data capabilities that power the next generation of analytics and AI solutions, we'd love to hear from you.

Requirements

Bachelor's degree in Computer Science, Data Science, Software Engineering, Statistics, Mathematics, Information Systems, or a related technical discipline and/or equivalent experience.
Minimum of 5 years' experience in Data Product Management, Data Architecture, Data Engineering, Analytics Engineering, or related technical product leadership roles.
Demonstrated experience delivering enterprise data products on modern cloud-based data platforms.
Strong expertise designing enterprise data models, including dimensional modeling, semantic modeling, and scalable analytical data structures supporting both Business Intelligence and AI applications.
Experience with modern data stack technologies, including Snowflake, dbt, Apache Airflow, SQL, and cloud-based ETL/ELT frameworks.
Advanced SQL skills with experience optimizing large-scale analytical datasets.
Experience working with AWS cloud services and modern cloud data architectures.
Experience implementing enterprise Data Governance practices, including metadata management, data lineage, business glossaries, master data management, and data quality frameworks.
Strong understanding of enterprise Business Intelligence platforms such as Tableau and other modern BI tools, such as Sigma or ThoughtSpot.
Demonstrated ability to translate complex business challenges into scalable analytical and technical solutions.
Excellent communication, collaboration, and stakeholder management skills across both technical and nontechnical audiences.

Nice To Haves

Experience partnering with Data Science or Machine Learning teams to deliver AI-enabled products.
Familiarity with Python (Pandas, NumPy, PySpark) for exploratory data analysis, data validation, or pipeline development.
Experience supporting machine learning workflows, including feature engineering, model lifecycle management, or MLOps practices.
Knowledge of modern AI data architectures, including vector databases, Retrieval Augmented Generation (RAG), embeddings, and semantic search.
Experience with Databricks, Redshift, Azure, Google Cloud, or other enterprise analytics platforms.
Familiarity with healthcare, medical device, or regulated industry data governance practices.
Understanding of Responsible AI, AI governance, and regulatory considerations for enterprise AI adoption.

Responsibilities

Work with Data Product peers to define and manage the roadmap for data products that support Business Intelligence, advanced analytics, data science, and AI initiatives across Cardiology.
Partner with stakeholders to identify strategic opportunities where data, predictive analytics, and AI can improve commercial performance, customer insights, operational efficiency, and decision making.
Translate business objectives into scalable data products, ensuring alignment between business strategy, data architecture, engineering, and analytics.
Collaborate with data engineers, architects, and data scientists to design scalable conceptual, logical, and physical data models that support reporting, machine learning, and AI-enabled applications.
Ensure enterprise data assets are AI-ready by improving data quality, metadata, lineage, governance, master data management, and semantic consistency, including management of semantic models across Cardiology Business Intelligence.
Partner with Data Science and AI Farm teams to understand feature engineering, model input requirements, and analytical workflows, ensuring data products effectively support machine learning development and deployment.
Guide the modernization of existing data assets into cloud-native architectures using modern data stack technologies, including Snowflake, dbt, Airflow, and related platforms.
Evaluate and recommend emerging technologies, including generative AI, large language models (LLMs), predictive analytics, and intelligent automation, to accelerate business value.
Partner to establish scalable operating models for Business Intelligence best practices to improve data reliability, reproducibility, and operational excellence.
Collaborate across Commercial IT, Digital Capabilities, Sales Strategy, and Business Intelligence teams to ensure data products deliver measurable business outcomes.
Communicate technical concepts, data strategies, and project progress effectively to technical teams, business stakeholders, and executive leadership.