Healthcare Data Scientist | 20 Years Experience | Doctoral Student, Johns Hopkins
Director of Data Science at ADLM and doctoral student at Johns Hopkins Bloomberg School of Public Health, specializing in algorithmic bias, health equity analytics, and patient safety in healthcare AI.
Director of Data Science | Association for Diagnostics & Laboratory Medicine (2025-Present)
Data Scientist | Health Resources and Services Administration, HHS (2023-2025)
- Contributed information on health equity to HHS AI strategy
- Led health equity analytics for federal Health Center Program (1,400+ FQHCs, 30M patients)
- Contributed to automated PHI detection system in Snowflake for UDS+ data submissions, developing pattern-matching algorithms to flag protected health information and ensure HIPAA compliance across 1,400+ health centers
Data Scientist | Accenture Federal Services (2021-2023)
- Built fraud detection models for 4M-beneficiary federal healthcare program achieving >90% AUC, $500M+ annual savings
- Developed COVID-19 forecasting dashboards for state health department leadership
Data Science Fellow | FDA Center for Veterinary Medicine (2020-2021)
- Built safety surveillance dashboard analyzing 1M+ adverse event reports
Regulatory Coordinator/Data Manager | UCLA, Georgetown, UW, Johns Hopkins
- Regulatory Coordinator for first-in-human Keytruda melanoma immunotherapy study at UCLA
- Contributed to research published in JAMA on pediatric transplant patients
Analytics: Python, R, SQL | Machine learning (scikit-learn, XGBoost, TensorFlow) | Tableau | Statistical modeling | Health equity methods
Healthcare Data: Claims (IBM Explorys), EHR, HRSA UDS, NHANES | Federal reporting standards (OMB, CMS, HRSA)
Data Engineering & Infrastructure: Snowflake, Azure Databricks, AWS, PostgreSQL | ETL pipelines | Automated data quality checks | REDCap
Domain: Algorithmic fairness, systematic reviews, clinical trial protocols, FDA/IRB compliance, social determinants of health
Doctor of Public Health (DrPH) | Johns Hopkins Bloomberg School of Public Health | Expected 2029
MS, Epidemiology | Georgetown University
Certificate, Data Science | Georgetown University
BA, Political Science | Wellesley College
HHS AI Strategy Archive - Federal AI governance documents from HHS AI Community of Practice participation
Data Science Best Practices - Black in Data Week 2023 presentation on reproducible research workflows
Portfolio projects in development: Federal health equity analytics templates, healthcare visualization examples, systematic review tools
Available for research collaboration in my capacity as a doctoral researcher:
Services:
- Health equity analytics and federal reporting compliance (HRSA UDS, CMS)
- Systematic literature reviews (search strategy, screening, PRISMA documentation)
- Algorithmic bias evaluation for healthcare prediction models
- Statistical consultation and manuscript development
- Data quality automation and HIPAA compliance support
Inquiry: [email protected]
- Pourat, N., Hobby, A., et al. (2024). HRSA-funded health centers reduce healthcare expenditures. AcademyHealth Annual Research Meeting
- Feldman, A.G., et al. (2019). Immunization in pediatric transplant patients. JAMA, 322(18), 1822-1824 [Data management acknowledgment]
- Panel Moderator: "AI Roadmap in Healthcare IT" - HIMSS 2024
- Presenter: "Data Science Best Practices" - Black in Data Week 2023
- Reviewer: HIMSS, Black in AI, NeurIPS (2024)
Newsletter: Health Innovation - Monthly insights on healthcare AI and health equity
LinkedIn: linkedin.com/in/andreahobby
ORCID: 0009-0003-8006-9605
Currently: Studying Applied Clinical Informatics & Patient Safety at Johns Hopkins | Building open-source health equity analytics tools
Research collaboration conducted in individual capacity as doctoral researcher, separate from institutional employment.