Ijeoma Dafe

Owns data infrastructure and reporting systems that power research, analytics, and partner-facing work — building core data models, schemas, ETL/ELT pipelines, and analysis-ready datasets with reproducible, well-documented workflows that cross-functional teams rely on every day. Designs, builds, and maintains dashboards, recurring reporting, and self-serve tools for internal and external stakeholders using SQL, Python, R, Tableau, Power BI, and AWS, with strong judgment about data quality, metric definition, and governance. Applies rigorous evaluation frameworks to emerging AI and tool-based interventions — building measurement, reporting, and analysis workflows that assess learner experience, reliability, safety, and outcomes.

Implements trusted metric definitions, documentation, and governance practices across reporting and research workflows, ensuring data accuracy, consistency, and auditability at scale. Leverages AI tools in data science workstreams to accelerate exploration, automate reporting, and surface insights. Collaborates cross-functionally with research, product, implementation, and operations teams, translating messy ambiguous data problems into clean, reproducible deliverables.

Delivers measurable outcomes: 85% model accuracy, 40% reduction in manual effort, 95% data quality compliance.

Data Analyst/Scientist - Gilead Services - Remote

(2023-09)

Health tech analytics environment

• Python, SQL, R, and cloud-based data infrastructure
• ETL/ELT pipeline development; dashboard and reporting systems
• AI tool evaluation; cross-functional partnership with research, product, and implementation teams.
• Owned core data models, schemas, and ETL pipelines from raw source ingestion through analysis-ready dataset production — applying reproducible, Git-versioned workflows with documentation and testing practices that ensured reliability for downstream research and reporting consumers.
• Designed, built, and maintained BI dashboards and recurring reports in Tableau and Power BI for internal teams and partner-facing deliverables, establishing self-serve analytics tools that empowered non-technical stakeholders to access trusted data independently.
• Led evaluation and measurement framework design for AI-powered analytics capabilities — defining baselines, selecting metrics, building analysis workflows, and monitoring for performance drift to assess tool quality, safety, and outcome impact in production environments.
• Implemented data quality checks, validation frameworks, and metric governance standards — ensuring accuracy, consistency, and auditability across all analytical outputs and achieving 95% internal control adherence across regulated datasets.
• Applied AI tools in data science workstreams to accelerate exploratory analysis, automate reporting narratives, and surface patterns — improving insight delivery speed by 50% for cross-functional research and product stakeholders.
• Collaborated cross-functionally with Research & Evaluation, Implementation, Operations, and Product teams — translating ambiguous data problems into well-scoped deliverables and communicating findings clearly to non-technical internal collaborators.

Business Intelligence Analyst - Northside Hospital - Atlanta, GA

(2018-07 - 2023-08)

Large academic health system

• HIPAA-regulated enterprise data environment
• Python, SQL, R, Tableau, and Power BI; data pipeline architecture and governance; research and evaluation analytics; executive and cross-functional reporting.
• Rebuilt and modernized core analytics infrastructure — designing ETL/ELT pipelines, data models, and schemas in Python and SQL that transformed raw clinical, financial, and operational data into reproducible, analysis-ready datasets used by research and leadership teams.
• Designed and maintained dashboards and self-serve reporting tools for both internal teams and external partner deliverables — building recurring reports with metric definitions and lineage documentation that ensured data quality and stakeholder trust across 1M+ record datasets.
• Supported quantitative data preparation and reproducible code workflows for impact and efficacy studies — applying statistical analysis in Python and R to longitudinal datasets and producing clean, well-documented analytical outputs aligned to research design requirements.
• Developed practical measurement approaches for new operational interventions — defining evaluation frameworks, success metrics, and monitoring workflows that translated ambiguous program goals into structured, evidence-based assessments.
• Established and stewarded trusted metric definitions, taxonomy standards, and governance practices across reporting workflows — reducing data inconsistency issues by 30% and ensuring reliable, auditability-compliant outputs across compliance and research deliverables.
• Partnered cross-functionally with clinical, finance, operations, and compliance teams — translating complex analytical findings into clear narratives for non-technical audiences and improving leadership decision speed by 50%.

Master of Science - Data Science - Walden University

Bachelor of Engineering - Computer Science and Engineering - Enugu State University of Science and Technology