Senior Data Strategy & Analytics Specialist

Roche • Boston, MA

3d • $107,400 - $199,400 • Onsite

About The Position

The Senior Data Strategy & Analytics Specialist is part of Roche Product Development’s Real-World & Clinical Data Strategy (RWCDS) team, which develops data strategies and analytics to support decision-making and evidence generation. Reporting to the Data Strategy & Analytics Lead, the specialist is a technical and strategic partner to cross-functional teams, translating scientific, clinical, translational, and business questions into analytical frameworks, cohort extractions, analytical code, and reproducible workflows. The role uses multimodal real-world data, clinical trial data for secondary use, and operational data to support therapeutic-area and molecule-portfolio strategy.

The specialist independently designs and executes advanced cohort extractions, complex transformations, and observational or exploratory analyses. They promote rigorous, reproducible, auditable analytical practices; assess and curate data sources; and support data harmonization, integration, and clinical-trial data marts. They communicate findings to project teams and strategic decision-makers, advise on study framing and evidence plans, develop reusable analytical resources, and mentor junior colleagues. The role also promotes careful adoption of AI coding tools while maintaining scientific rigor and code quality.

The position is based in Boston, Massachusetts. Relocation benefits are not available. The stated annual salary range is $107,400 to $199,400, with a discretionary annual bonus potentially available based on individual and company performance. Benefits are referenced in the posting but are not itemized.

Requirements

• Master’s or PhD in Quantitative Health Sciences (such as Biostatistics or Epidemiology), Data Science, or a closely related quantitative field.
• At least 3 years of experience working with structured healthcare data, including real-world data, clinical-trial data, or observational research, in a biopharmaceutical, academic research, or healthcare setting.
• Advanced, demonstrated proficiency in R or Python.
• Strong working expertise in SQL for complex database querying and data wrangling.
• Working knowledge of metadata management, data standardization, or data mapping approaches.
• Ability to translate research or operational needs into data requirements and workflows.
• Independent thinking and ability to make decisions based on sound principles.
• Strategic agility, problem-solving, and critical-thinking skills extending beyond the technical domain.
• Respect for cultural differences when interacting with colleagues in a global workplace.
• Excellent verbal and written communication skills, including presentation and writing, and ability to explain complex technical concepts clearly.

Nice To Haves

• Domain expertise or prior research experience in cardiovascular, renal, or metabolic (CVRM) disease areas, clinical endpoints, and disease-specific biomarkers.
• Advanced understanding of observational study designs, confounding adjustment such as propensity scores and matching, censoring, time-to-event or survival analysis, and descriptive biostatistics.
• Practical experience building, transforming, or curating datasets using the OMOP Common Data Model or CDISC standards such as SDTM and ADaM.
• Advanced knowledge of medical coding classifications and ontologies such as ICD-9/10, SNOMED CT, RxNorm, LOINC, CPT, and HCPCS, including real-world mapping nuances.
• Practical experience with Git/GitHub, CI/CD, containerized environments, and safe adoption of AI developer tools such as GitHub Copilot or Claude Code.
• Track record as a consultative partner to clinical, observational, or translational research scientists in shaping study questions, evidence generation, and analytical strategy.

Responsibilities

• Independently design and execute advanced cohort extractions, complex data transformations, and observational or exploratory analyses across real-world, secondary clinical-trial, and operational multimodal data.
• Translate scientific, translational, and business questions into cohort definitions, derived variables, and analysis-ready datasets.
• Promote coding standards, version control workflows, automated testing, and structured quality-control routines for reproducible and auditable analytical pipelines.
• Synthesize and communicate analytical outputs, cohort dynamics, and study findings to project teams and strategic decision-makers.
• Partner with RWD Scientists and cross-functional study leads on study framing, protocol development, and Integrated Evidence Plan creation, advising on feasibility, methodological rigor, and analytical strategy.
• Develop reusable code libraries, analytical frameworks, and best-practice templates, and mentor junior team members.
• Evaluate and curate fit-for-purpose real-world data sources, including data quality assessments, terminology mappings, and schema transformations.
• Partner with study teams and data engineering functions to define requirements, validate derived variables such as ADaM and ARDs, and design clinical-trial data marts for secondary use.
• Lead data harmonization and multimodal integration with data engineering and governance teams, using healthcare common data models such as OMOP and CDISC.
• Analyze data architectures, oversee lineage documentation, and establish compliant, accessible, reusable data assets that support FAIR principles and long-term data strategy.
• Promote safe use of AI coding tools and LLM assistants while safeguarding scientific rigor and code quality.

Benefits

• A discretionary annual bonus may be available based on individual and company performance.
• Benefits are referenced in the posting, but specific benefits are not itemized.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior