Senior Biostatistician

Senior Biostatistician Truveta is the world’s first health provider led data platform with a vision of Saving Lives with Data. Our mission is to enable researchers to find cures faster, empower every clinician to be an expert, and help families make the most informed decisions about their care. Achieving Truveta’ s ambitious vision requires an incredible team of talented and inspired people with a special combination of health, software and big data experience who share our  company values . Truveta was born in the Pacific Northwest, but we have employees who live across the country. Our team enjoys the flexibility of a hybrid model and working from anywhere.  In person attendance is required for one week during the year for Truveta Planning Week. For overall team productivity, we optimize meeting hours in the pacific time zone. We avoid scheduling recurring meetings that start after 3pm PT, however, ad hoc meetings occur between 8am-6pm Pacific time. #LI-remote Who We Need   Truveta is rapidly building a talented and diverse team to tackle complex health and technical challenges. Beyond core capabilities, we are seeking problem solvers, passionate and collaborative teammates, and those willing to roll up their sleeves while making a difference. If you are interested in the opportunity to pursue purposeful work, join a mission-driven team, and build a rewarding career while having fun, Truveta may be the perfect fit for you.  This Opportunity We are seeking a Senior Biostatistician with expertise in the design, analysis, and interpretation of studies using electronic health records (EHR) and administrative claims data. This role will support a wide range of real-world data (RWD) projects including retrospective and prospective cohort studies, comparative effectiveness research, HEOR studies, and more. The ideal candidate is highly proficient in using real-world data and advanced statistical methods, and thrives in a collaborative, data-intensive environment. Key Responsibilities Study Design & Data Strategy Collaborate with the Research Services team to design and develop statistical analysis plans (SAPs) tailored for EHR and claims-based studies, and provide input into study protocols Review and develop methods to validate variable definitions, cohort definitions, and various data elements. Data Analytics & Programming Conduct complex statistical analyses (e.g., time-to-event analysis, longitudinal data analysis, causal inference methods for observational and quasi-experimental designs such as propensity score matching, inverse probability weighting, and difference-in-difference) using R or Python. QC analyses and check assumptions of statistical models to ensure correct implementation of analyses and ensure quality and robustness of results. Review methods for data wrangling and transformation of large-scale EHR and claims data using SQL, R, or Python. Support data cleaning as needed. Develop and review reusable analytic pipelines and tools for scalable RWD analysis. Reporting & Communication Interpret statistical findings in the context of real-world data limitations (e.g., missingness, measurement error). Generate clear and actionable insights via statistical reports, dashboards, or manuscripts. Present results to internal teams, clinicians, external stakeholders, and research collaborators. Technical & Scientific Leadership Provide statistical mentorship and code review for junior team members. Provide thought leadership for how we can better leverage real-world data for research studies (ex: how can we fine-tune methods to better adjust for confounders, infer causality, and improve data quality?) . Ensure adherence to industry best practices and reproducible research principles. Required Skills Master’s or Ph.D. in Biostatistics, Mathematics, Epidemiology, Health Economics, Data Science, or related field. 5+ years of applied statistical experience with EHR and/or claims data (e.g., Optum, MarketScan, Flatiron, Epic, Cerner, CMS ). Strong programming skills in R and SQL; experience with Python, PySpark, or Databricks a plus. Proven expertise in working with structured health data using medical ontologies such as ICD-10, CPT, NDC, LOINC, and RxNorm. Experience with advanced methods such as: Propensity score matching or inverse probability weighting Survival time-to-event analysis (e.g., Cox proportional hazards model , Fine-Gray model, etc. ) Generalized linear and mixed-effects models Difference-in-differences or interrupted time series Understanding of biases in RWD (e.g., immortal time bias, selection bias, etc.) Strong communication skills (written and verbal) Preferred Qualifications Experience supporting regulatory-grade RWE, HEOR, or payer-facing studies. Exposure to clinical phenotyping, NLP, or unstructured data integration. Ability to translate statistical output into clinicall