Opportunities

Our group offers a variety of academic positions across our multidisciplinary team.

Featured Jobs

Research Scientist/Data Analyst (Real-World Data & Causal Inference)

The Williams Research Group at Stanford University and Harvard Chan School seeks a full-time Research Data Analyst to support a portfolio of population health studies that use large-scale real-world data to advance women’s health, reproductive, pediatric, and perinatal epidemiology, and cardiometabolic disease across the life course. The analyst will design and execute analyses on administrative claims and electronic medical record (EMR) databases, build reproducible analytic pipelines, and apply modern causal inference methods to questions with direct clinical and policy relevance. This role suits someone with formal epidemiology and/or biostatistics training who is comfortable owning a dataset end-to-end from raw extraction through analysis-ready cohorts to publication-quality results.

Department: Epidemiology & Population Health, Stanford University School of Medicine

Key Responsibilities

• Independently manage, clean, link, and analyze large real-world datasets, including insurance claims (e.g., MarketScan) and EMR/federated clinical data (e.g., Epic Cosmos, STARR).
• Construct analytic cohorts and phenotypes from administrative and clinical data, including code-based algorithm development (ICD, CPT, NDC) and validation.
• Apply rigorous epidemiologic and causal inference methods—directed acyclic graphs (DAGs), target trial emulation, propensity score and g-methods, mediation/decomposition analyses—under the direction of the PI and senior collaborators.
• Develop and maintain reproducible, well-documented code in R, SAS, Python, and SQL, using version control.
• Generate tables, figures, and visualizations suitable for manuscripts, grant applications, and presentations.
• Contribute to study design, analysis plans, and the methods and results sections of manuscripts and grant proposals; co-authorship on publications where warranted.
• Maintain compliance with data use agreements, IRB protocols, and data security requirements.
• Collaborate with faculty, postdoctoral fellows, students, and external research partners; communicate methods and findings clearly to both technical and non-technical audiences.

Required Qualifications

• PhD degree (or equivalent) in epidemiology, biostatistics, data science, or a related quantitative discipline.
• Demonstrated experience analyzing large real-world health datasets—claims data, EMR data, or both.
• Strong programming proficiency in at least two of: R, SAS, Python, SQL, with the ability to work with datasets at scale (millions of records).
• Solid grounding in epidemiologic study design and biostatistical methods.
• Excellent organizational skills, attention to detail, and the ability to manage multiple projects to deadline with minimal supervision.
• Outstanding written and oral communication skills, with the ability to communicate technical information to both technical and non-technical audiences.

Preferred Qualifications

• Formal training or demonstrated competence in causal inference (DAGs, target trial emulation, g-methods, propensity score methods, mediation analysis).
• Experience with federated or common data model platforms (Epic Cosmos, OMOP/OHDSI, PCORnet, TriNetX) and/or large claims databases (MarketScan, Optum, Medicare/Medicaid).
• Background in women’s health, perinatal/reproductive epidemiology, cardiometabolic disease, or pharmacoepidemiology.
• Experience with phenotype/algorithm development and validation.
• Familiarity with reproducible-research workflows (Git, R Markdown/Quarto) and high-performance or cloud computing environments.
• Track record of co-authored peer-reviewed publications.

To Apply
Please submit a cover letter, CV/résumé, a code sample or GitHub link, and at least one writing sample or peer-reviewed publication. Review of applications will begin immediately and continue until the position is filled.

Postdoctoral Fellow in the Epidemiology Branch at NICHD

The Epidemiology Branch (EB) at the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Intramural Population Health Research Program is recruiting outstanding postdoctoral fellows to join a multidisciplinary team whose mission is to conduct original research focusing on human reproduction, pregnancy, and maternal and child health. Candidates will train for a career in epidemiologic research under the mentorship of EB Branch Chief, Dr. Bizu Gelaye (adjunct professor of epidemiology at Harvard Chan School), or Senior Investigators, Drs. Katherine Grantz, Edwina Yeung, and Fasil Tekola-Ayele. Fellows will develop their projects within the scope of ongoing research led by their mentor using population-based datasets to study topics in psychosocial exposures, perinatal mental health, fetal growth, infertility treatment, placental epigenetics, and maternal and offspring health outcomes. Please see the position post for details.