Senior Data Scientist

Boston Scientific•Georgetown, MA

1d•$89,200 - $169,500•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.
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.