Data Strategy & Analytics Specialist

Roche•Boston, MA

19h•$89,400 - $166,000•Onsite

About The Position

This role is based in the Real-World & Clinical Data Strategy (RWCDS) team which drives data excellence and analytics to power enhanced decision-making and evidence-generation within Roche Product Development. We develop and implement data strategies that unlock the value of clinical, operational, and real-world data to accelerate evidence generation, decision-making, and innovation across PD and beyond. By embedding FAIR principles, advancing data governance, and enabling data productization, RWCDS ensures that data is not only findable and usable, but also a driver of scientific rigor, regulatory readiness, and AI-powered transformation. Through deep partnerships with internal functions and external collaborators, we shape a future-ready data ecosystem that supports personalized healthcare and efficient drug development. The Data Strategy & Analytics Specialist contributes to the design and execution of data strategies and analyses that enable decision-making and insight generation across a therapeutic area or molecule portfolio. Working under the Data Strategy & Analytics Lead, you will partner with cross-functional teams—translating scientific protocols into robust analytical code, cohort extractions, and reproducible pipelines using multi-modal real-world data and clinical assets. You will also contribute to the translation of scientific and business needs into analytical frameworks and data-driven solutions. This role demands strong core data science skills, analytical execution, and a growth mindset to continuously elevate team standards.

Requirements

Bachelor’s or Master’s degree in Informatics, Life Sciences, Computer Science, or a related field
2+ years of experience working with structured data in a research or healthcare setting
Solid working knowledge of R or Python, as well as SQL
Understand data dictionaries, mappings, or metadata tagging
Familiarity with data systems used in clinical or operational settings
Demonstrate capacity for independent thinking and ability to make decisions based upon sound principles
Exhibit excellent strategic agility including problem-solving and critical thinking skills, and agility that extends beyond the technical domain
Demonstrate respect for cultural differences when interacting with colleagues in the global workplace
Possess excellent verbal and written communication skills, specifically in the areas of presentation and writing, with the ability to explain complex technical concepts in clear language

Nice To Haves

Working knowledge of descriptive statistical methods and epidemiological concepts used in observational healthcare research (e.g., cohort identification, censoring, time-to-event concepts).
Prior experience in cardiovascular, renal, or metabolic (CVRM) therapeutic areas.
Familiarity with clinical terminologies and coding systems, including ICD, SNOMED CT, RxNorm, CPT, and HCPCS.
Exposure to real-world data common data models (e.g., OMOP CDM) or CDISC standards (SDTM, ADaM).
Familiarity with software engineering best practices (Git, unit testing) and AI-assisted coding tools (e.g., Claude Code, GitHub Copilot).

Responsibilities

Execute data transformations, cohort extractions, and descriptive/observational analyses leveraging real-world (EHR, Claims, etc.), clinical trial data for secondary use, and operational multi-modal domains.
Translate analytical or study requirements into cohort definitions, derived variables, and analysis-ready datasets.
Follow state-of-the-art coding best practices, version control, unit testing, and structured QC routines to guarantee fully reproducible analytical pipelines.
Summarize and visualize exploratory analytical outputs, cohort characteristics, and study results to communicate findings effectively to project teams.
Partner seamlessly with RWD Scientists and cross-functional study leads during study framing and protocol development, providing active feedback on data availability, feasibility, methodology, and execution.
Actively contribute well-documented code snippets, analytical templates, and learnings to the internal team repository.
Assist in identifying fit-for-purpose real-world data (RWD) sources, performing data quality evaluations, terminology mappings, and schema transformations for secondary data use.
Partner with study teams and central data engineering functions to gather analysis requirements, specify derived variables (e.g., ADaM, ARDs), and shape clinical trial datamarts for secondary use.
Collaborate with central data engineering and governance teams to guide multi-modal data integration, applying common data models (OMOP, CDISC) and structured mappings across clinical trial and real-world assets.
Collaborate with internal partners to analyze data structures, document data lineage, and maintain structured, compliant, and accessible data assets to support long-term data strategy and reusability.
Safely leverage modern AI coding tools and LLM assistants (e.g., Claude Code, Copilot) to accelerate scripting, query building, and data extraction while maintaining scientific rigor and code quality.