Clinical Data Scientist, Real World Evidence

Boston Scientific•Maple Grove, MA

14h•Hybrid

About The Position

The Clinical Data Scientist, Real World Evidence (RWE) will design and deliver advanced analytics solutions that generate robust, transparent, and scalable real-world evidence. This role operates with a high degree of autonomy across complex and ambiguous problems, leading sophisticated analyses, developing and validating novel methodologies, and operationalizing analytics across diverse real-world data sources. As a key contributor within the Global RWE function, this individual will lead analytic strategy for priority initiatives, serve as a subject-matter expert, and mentor team members. The role partners closely with scientific, clinical, regulatory, and data stakeholders to translate data into actionable evidence and advance RWE analytics capabilities across the organization.

Requirements

Minimum Bachelor’s degree or advanced degree in data science, biostatistics, epidemiology, computer science, or a related field, or equivalent experience.
Minimum of 8 years' with Bachelor’s degree experience or 5 years with a Master’s degree applying advanced analytics to real-world health care data, including electronic health records, claims, or registries
Proven proficiency in modern data science tools and programming languages (e.g., Python, R, SQL)
Demonstrated expertise in statistical and machine learning methods relevant to real-world evidence
Proven experience communicating analytic findings to cross-functional and senior stakeholders (Clinical, R&D, Health and market access teams)

Nice To Haves

Proven experience supporting regulatory-aligned evidence generation.
Proven experience working in a global or matrixed environment.

Responsibilities

Translate clinical and scientific questions into robust analytic plans and reproducible pipelines using EHRs, claims, registries, and other real‑world data.
Lead data integration, causal inference, survival and comparative effectiveness analyses, feasibility assessments, and outcomes research with rigorous validation.
Serve as analytic lead for priority studies, guiding study design and ensuring fit‑for‑purpose, regulatory‑aligned evidence.
Develop and deploy advanced analytics, including predictive modeling, AI/ML, phenotyping, and NLP for unstructured data.
Design clear data visualizations and dashboards to communicate insights.
Partner with data engineering teams to enable scalable analytics environments and pipelines; lead pilots to assess new data sources and methods.
Build reusable, version‑controlled pipelines with strong quality checks, documentation, and lineage.
Implement validation and monitoring of data, analyses, and models over time.
Contribute to data governance and champion reproducible research and code review best practices.
Support interpretation and communication of results through reports, publications, presentations, and regulatory‑aligned materials.
Present complex analyses clearly to technical and non‑technical stakeholders.
Help resolve data and infrastructure issues supporting study execution.
Partner cross‑functionally to align RWE analytics with organizational priorities.
Act as a trusted analytic advisor influencing evidence strategy and prioritization.
Mentor analysts and balance near‑term delivery with long‑term capability building.