Professional Resume Template for
Longitudinal Data Analyst
Julian T. Mercer
Boston, MA
(617) 555-0143
julian.mercer@email.com
linkedin.com/in/julian-mercer | github.com/julianmercer | julianmercer.dev
Professional Summary
Methodical Longitudinal Data Analyst with 6 years of experience designing and executing statistical models for complex cohort studies and clinical research. Track record includes analyzing datasets of up to 1.2M records, applying mixed-effects and GEE modeling to capture trajectories across 45,000+ repeated measures, and authoring statistical reports that accelerated clinical trial approvals by 25%. Expert in managing missing data strategies, survival analysis, and cohort matching techniques. Adept at programming advanced statistical pipelines using R, SAS, SQL, Stata, and Python.
Work Experience
Longitudinal Data Analyst — Beacon Health Research Institute
Boston, MA | January 2024 – Present
- Modeled cohort data for a 5-year multi-center clinical study of 12,000 patients using R (lme4 and survival), reducing error rate in longitudinal trajectory estimates by 18%.
- Constructed mixed-effects models and generalized estimating equations (GEE) in SAS (PROC GLIMMIX and PROC GEE) to analyze 45,000 repeated-measure observations, accelerating report delivery by 14 days.
- Cleaned and restructured unstructured electronic health record (EHR) databases of 1.2M rows using SQL and tidyverse, improving data processing efficiency by 32% and reducing missingness bias.
- Coordinated statistical planning with 8 clinical investigators to define covariates for survival analysis protocols, decreasing time-to-protocol-approval by 25%.
Associate Longitudinal Data Analyst — Vanguard Clinical Services
Boston, MA | September 2021 – December 2023
- Analyzed longitudinal survey data from a national health cohort of 8,500 participants using Stata and R, identifying 3 key demographic risk factors with a 95% confidence interval.
- Developed automated SAS macros for repeated measures ANOVA and PROC MIXED, reducing manual statistical reporting times by 40% across 5 active research projects.
- Prepared 15 comprehensive statistical sections for FDA drug submissions and peer-reviewed journals, achieving a 100% compliance rate with CDISC and SDTM standards.
- Reshaped and consolidated 4 legacy databases containing 500,000 longitudinal records into a standardized Postgres warehouse, reducing query runtimes by 28%.
Education
Master of Science in Biostatistics
Northeastern University · Boston, MA · 2020
Bachelor of Science in Mathematics
Redwood State University · Naperville, IL · 2018
Skills
Longitudinal data analysis, Mixed-effects models, Generalized estimating equations (GEE), Growth curve modeling, Survival analysis, Time-series analysis, Missing data imputation, Biostatistics, Clinical trial design, R programming, SAS programming, SQL, Python, Stata, Git, Tableau, OMOP Common Data Model
Projects
Cardiovascular Health Cohort Study
Role: Lead Biostatistician
Tools: R, SAS, SQL, Git
Led statistical analysis of a 10-year cohort study of 25,000 patients, identifying 5 key longitudinal risk factors and publishing findings in JAMA.
EHR Data Standardization Initiative
Role: Data Integration Analyst
Tools: Python, SQL, PostgreSQL, AWS
Standardized 5 million rows of unstructured clinical trial records into an OMOP Common Data Model, reducing data preparation times for analyst teams by 45%.
Certifications
- Professional Statistician (PStat) (2024)
- SAS Certified Statistical Business Analyst Using SAS 9 (2021)
Additional information
- Languages: English (Native), French (Conversational)
- Volunteer Work: Guest lecturer on statistics at local high school STEM programs (2022–present)
- Professional Affiliations: Member of the American Statistical Association (ASA)
Job Market Insights
Market data and opportunities for
Longitudinal Data Analyst
Job Market Insights
$85,000
-
$135,000
Avg:
$110,000
Growth Outlook:
Employment of statisticians and data analysts specializing in longitudinal research is projected to grow by 30% from 2024 to 2034, driven by the expanding volume of real-world evidence and complex clinical trials. While AI and machine learning tools automate basic data cleaning and visualization, human statistical expertise remains critical. Organizations require analysts to select valid covariance structures, manage complex missing data mechanisms, and translate statistical trajectories into actionable healthcare policy and clinical interventions.
30% growth over 10 years
Key Skills Required
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