Data Reporting & Analytics Consultant, Programming Serve as a biostatistician/senior statistical analyst supporting real-world and observational studies in chronic disease, oncology, and vaccine safety. Develop statistical analysis plans (SAPs), define cohorts using ICD, CPT, and NDC codes, and apply advanced statistical methods—such as Cox proportional hazards models, propensity score weighting/matching, and logistic regression—to evaluate treatment effectiveness and health outcomes. Using SAS, R, and SQL, create and validate ADaM-style analysis datasets, generate publication-quality tables, listings, and figures (TLFs), and contribute to manuscripts in collaboration with academic and industry partners like Harvard and AstraZeneca. My work also includes modeling healthcare cost outcomes and supporting cross-site studies on environmental risk exposures and cancer incidence. • Lead statistical programming and analysis using extensive EHR/claims data across 10+ clinical research studies on medication efficacy and safety to deliver insight for internal quality improvement and external research collaborations with AstraZeneca, the Veterans Affairs, and Harvard University etc. • Create CDISC SDTM and ADaM-like datasets ready for analysis for peer-reviewed publications and internal deliverables, ensuring traceability and consistency; supported over 10 publications on real-world efficacy, safety, and cost-effectiveness of hypertension treatments. • Generate and validated TLFs, implemented data transformation, and conducted analyses following SAPs. • Collaborate with global cross-functional teams, including biostatisticians, investigators, and clinicians. • Build and maintained 10+ standardized SAS macros in a Unix environment and R functions in R server that improved reproducibility and reduced programming time by 20%. • Extract and process large-scale EHR and claims data from Oracle databases, registries, pharmacy records, and other external data sources using SQL. • Derive analysis variables (e. • , exposures, outcomes, comorbidities) using ICD/CPT codes, lab and vital records, and GPI/NDC drug codes. • Performe time-to-event analyses using Cox models and propensity score methods to evaluate treatment effects and disease incidence. • Create advanced visualizations for reports using R Markdown, Spotfire dashboards, and SAS GRAPH, including dashboards to support trend analysis and monthly provider performance evaluations. Skills: 1. R Programming 1. Attention to Detail 1. Regulatory Submissions 1. Data Quality Assurance 1. Statistical Analysis Plans 1. Clinical Trial Analysis 1. SQL 1. R (Programming Language) 1. SAS (Software)