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Role Description

The Real World Data Analyst role is a high-impact, intellectually challenging position that requires a diverse set of analytical and technical skills. It offers a unique opportunity for professional growth through access to an unprecedented volume of high-quality, real-world Electronic Health Record (EHR) data. In this role, you will tackle meaningful healthcare challenges using an EHR database encompassing more than 125 million patients—and growing—helping drive insights that can ultimately improve patient care. This position reports to the Senior Director of Research Services.

• Translate study protocols and statistical analysis plans (SAPs) into reproducible analytical code, executing end-to-end research studies from data extraction and wrangling through statistical analysis and results generation using SQL, R, or Python within agreed timelines.
• Review and interpret study protocols and SAPs, ensuring analytical approaches are implemented accurately and consistently with study objectives.
• Develop robust data pipelines for cohort building, data cleaning, feature engineering, and preparation of analytical datasets using large-scale real-world data sources.
• Conduct rigorous quality control (QC) of code, datasets, and statistical outputs, including verification of model assumptions, reproducibility of results, and validation of analytical approaches.
• Proactively identify and communicate potential data issues, methodological concerns, or deviations from study protocols early in the research process, recommending solutions and mitigation strategies.
• Collaborate closely with biostatisticians and researchers to support the development, refinement, and review of statistical analysis plans and analytical methods.
• Interpret analytical results and contribute to the synthesis of findings, ensuring outputs are scientifically sound and aligned with study objectives.
• Partner with life sciences customers and internal teams to execute high-quality analyses that support peer-reviewed publications, regulatory submissions, and scientific research.
• Provide clear, proactive communication on study progress, analytical challenges, and timelines, ensuring stakeholders remain informed and aligned.
• Work closely with cross-functional teams across Truveta to investigate data sources, validate data integrity, and support scientific credibility of research outputs.
• Deliver feedback to internal teams based on research experiences and customer needs to help inform improvements to Truveta’s data platform and research capabilities.

Qualifications

• Undergraduate or graduate degree in biostatistics, epidemiology, clinical research, data science, clinical informatics, or a related field.
• 3+ years of experience conducting analyses using large healthcare datasets (e.g., electronic health records or other real-world data sources).
• Strong proficiency in SQL and at least one statistical programming language (R or Python) for data wrangling, analysis, and reproducible research workflows.
• Experience translating research protocols or statistical analysis plans into executable analytical code.
• Experience performing rigorous quality control of analytical datasets, code, and statistical outputs.
• Demonstrated experience working with complex healthcare data involving millions of patient records.
• Experience collaborating with research teams or customers to deliver high-quality scientific analyses.
• Strong problem-solving skills and the ability to identify data or methodological issues early and communicate them effectively.
• Excellent written and verbal communication skills, including the ability to clearly explain analytical approaches and findings to technical and non-technical stakeholders.
• Ability to learn quickly and adapt in a dynamic, fast-paced environment.
• Willingness to learn new analytical tools, programming languages, and proprietary platforms used to analyze real-world data.

Preferred Qualifications

• Experience applying causal inference methods to observational healthcare data, including approaches such as propensity score matching/weighting, inverse probability weighting, marginal structural models, or instrumental variable analysis.
• Familiarity with study designs used to estimate causal effects in real-world data, such as target trial emulation, difference-in-differences, regression discontinuity, or interrupted time series.
• Experience evaluating confounding, selection bias, and other threats to causal validity in observational studies.

Benefits

• Interesting and meaningful work for every career stage
• Great benefits package
• Comprehensive benefits with strong medical, dental and vision insurance plans
• 401K plan
• Professional development & training opportunities for continuous learning
• Work/life autonomy via flexible work hours and flexible paid time off
• Generous parental leave
• Regular team activities (virtual and in-person)