Shalom Zvidzwa
San Francisco
Summary
Results-driven data analyst with strong SQL and Python skills. Experienced in predictive modeling and data visualization, delivering actionable insights that enhance decision-making and optimize program performance.
Overview
6
6
years of professional experience
Work History
Data Science Research Intern
University of the Pacific
Stockton
01.2026 - Current
- Improved research decision-making accuracy by designing and executing A/B tests and controlled experiments, validating intervention effectiveness and reducing bias in program evaluations.
- Generated actionable insights for faculty and stakeholders by performing large-scale EDA and statistical analysis on multi-source datasets, uncovering performance signals and behavioral trends.
- Strengthened causal inference and model reliability by applying feature selection, regularization, and cross-validation techniques to minimize overfitting and data leakage.
Clinical Research Data Analyst
UC Davis
Sacramento, CA
06.2023 - 08.2025
- Reduced reporting latency and audit findings by building reproducible ETL workflows and version-controlled documentation systems, ensuring regulatory-ready datasets and transparent analytic lineage.
- Improved patient safety and operational decision-making by integrating predictive risk models and anomaly detection methods to identify emerging safety signals and workflow breakdowns.
- Increased leadership confidence in analytical outputs by implementing governance frameworks that minimized bias, leakage, and overfitting in performance and forecasting models.
- Accelerated stakeholder alignment by building interactive Power BI and Tableau dashboards that surfaced KPIs, experimental results, and risk indicators in near real time.
Clinical Research Data Specialist
Stanford
Palo Alto, CA
06.2020 - 05.2023
- Reduced operational and compliance risk by designing automated SQL and Python data pipelines integrated with REDCap and Power BI, enabling real-time monitoring of enrollment signals, protocol deviations, and adverse event trends across multi-site studies.
- Increased grant funding competitiveness by developing cost models, enrollment projections, and risk-adjusted feasibility analyses, strengthening data-driven justifications for NIH and institutional research proposals.
- Improved early detection of enrollment bottlenecks and protocol non-adherence by applying exploratory data analysis and trend modeling, allowing study leadership to intervene before performance signals degraded.
Education
Master of Science - Data Science
University of The Pacific
Stockton, CA05-2026
Bachelor of Science - Biochemistry
Castleton University
Castleton, VT05-2020
Skills
Analytics & Modeling
Data Engineering & Pipelines
Visualization & Reporting
Clinical & Research Analytics
Cloud & Platforms
Business & Decision Analytics
Programming and Querying: SQL, Python, R
Libraries & Frameworks: Pandas, NumPy, Scikit-learn, Matplotlib Flask
Timeline
Data Science Research Intern
University of the Pacific
01.2026 - Current
Clinical Research Data Analyst
UC Davis
06.2023 - 08.2025
Clinical Research Data Specialist
Stanford
06.2020 - 05.2023
Master of Science - Data Science
University of The Pacific
Bachelor of Science - Biochemistry
Castleton University