Joy Njoroge

Managed end-to-end QA and rubric compliance for AI datasets. Diagnosed system anomalies and delivered data verification where previous workflows stalled.

QA & Integrity: Managed task revisions, ensuring strict compliance for AI inputs.
Problem Solving: Audited and optimized data outputs under tight deadlines.
Upskilling: Mastered complex, evolving AI rubrics rapidly.

Assisted in optimizing scalable data infrastructure and automated pipelines to process high-volume financial data for business intelligence.

ETL & Data Modeling: Supported the design of ETL procedures and SQL data models to feed internal dashboards.
Data Quality: Implemented validation frameworks to ensure data integrity.
Tech: Python, SQL, Linux.

Supported actuarial risk analysis and financial modeling workflows across insurance operations to drive data-backed decision-making.

Analysis & Reporting: Prepared comprehensive variance analysis and risk reports to evaluate operational exposure.
Data Optimization: Cleaned and structured large historical datasets to improve the accuracy of financial forecasting models.

In Progress: Pursuing advanced training in mathematical modeling, algorithmic alpha generation, and portfolio optimization workflows.

Quantitative Research: Designing and running risk simulations and factor testing models.
Data Infrastructure: Building customized tools for data extraction, web scraping, and pipeline normalization.

Key Focus: Building scalable data pipelines, containerization, and Infrastructure as Code (IaC).

Hands-on Stack: Docker, Terraform, SQL, Python, and cloud-hosted development in GitHub Codespaces/VS Code.

Completed intensive training focused on building scalable web applications, backend APIs, and modern data architectures.

Backend & APIs: Engineered full-stack services and RESTful APIs using Python, Flask, and PostgreSQL.

Workflow & Tools: Applied software engineering best practices, version control (Git), and containerized workflows (Docker).

Graduated with honors. Developed a deep foundation in quantitative finance, mathematical optimization, and statistical modeling.
Core Competencies: Specialized in financial risk modeling, predictive analytics, and portfolio simulation algorithms.
Technical Application: Applied rigorous data analysis techniques to translate complex, stochastic variables into actionable predictive frameworks.