Senior Data Scientist

Architects AWS-based clinical analytics platforms using Python, R, and Apache Spark to process longitudinal EHR and insurance claims data. Builds reproducible ETL workflows that normalize and link ICD, CPT, and NDC-coded records for multi-site cohort, treatment-pattern, and clinical outcome studies.

Develops survival and regression models, including Cox proportional hazards and accelerated failure-time models, and produces publication-quality survival curves, forest plots, and Kaplan-Meier summaries through ggplot2 and R Markdown. Automates scheduled reporting with Shiny and Apache Airflow, supports digital pathology feature engineering, and maintains AWS research pipelines using S3, Glue, Lambda, Git, and CI/CD.

Collaborates with clinicians, statisticians, bioinformaticians, product, QA, and security teams to deliver HIPAA-aligned analytics capabilities. Mentors junior analysts through pull-request reviews and office hours, authors methodology documentation, and helped migrate legacy SAS claims workflows to Python and R on AWS.

Built batch and streaming data pipelines in Python and Apache Spark on AWS to consolidate point-of-sale, loyalty, and web-event data into a Redshift analytics warehouse for merchandising teams.

Designed governed dbt staging, intermediate, and mart models and operationalized Apache Airflow DAGs with sensors, retries, and backfill patterns. Implemented CI/CD with GitHub Actions and Terraform-managed environments, and mentored analysts on PostgreSQL performance and dbt model testing.

Delivered freelance client engagements as a software engineer, building data-heavy web applications in Python and Django and integrating third-party APIs to ship end-to-end product features.

Built internal analytics tools and self-service operational dashboards using R, Python, tidyverse, and ggplot2. Designed PostgreSQL schemas and SQL queries for product analytics, retention funnels, and cohort reporting.

Containerized services with Docker and deployed reproducible AWS environments using EC2, RDS, and S3. Collaborated remotely with designers, product managers, and QA teams across time zones through Git-based code review and support runbooks.