Hello, I'm Hope Tatenda Mutema
A passionate data analyst, aspiring actuary, and curious AI enthusiast.
I love turning messy datasets into clear narratives, designing risk-aware models, and experimenting with agentic AI systems that help organizations make smarter, faster decisions.
Recently, I have been exploring deep research agents, financial and insurance analytics, and ways to bring production-ready AI into real products and workflows.
Welcome to my corner of the internet where analytics, risk, and creative AI experiments meet.
Major Projects
Civil Society & CRPD: NLP Text Analysis
Synthesized 337 reports from OHCHR. Used a custom NLP pipeline to map regional policy variations and vulnerability patterns across global disability reporting.
Insurance Lapse: Predictive Modeling
Analyzed 228k records using Random Forest and LASSO. Optimized detection of at-risk policyholders by 2x through threshold tuning for revenue protection.
Drivers of Mortality: Tableau BI Study
OLS regression across 3,183 counties identifying income inequality and poverty as primary death predictors over healthcare access.
Other Projects
Global correlation analysis between Corruption (CPI) and Human Development (HDI). Built a publication-ready scatter plot using R to highlight governance trends and outliers.
with Pydantic AI
Built a multi-step research orchestrator using Pydantic AI and GPT-5-mini. Generates structured, evidence-based reports on stock tickers and complex topics.
Risk Modeling
Built cost models in Python to quantify how age, BMI, smoking status, and region influence medical charges and actuarial risk tiers.
Regression Analysis
Applied multiple regression in R to analyze how genre, viewing hours, release timing, and seasonality impact global ratings.
Pathfinder AI
Sustainable Impact ChallengeBuilt an AI-powered platform using Agentic workflows with n8n to automate data extraction and deliver personalized academic recommendations for DC students.
Reach Analysis
Applied Chi-square tests & regression in R to explore statistical relationships between movie ratings and worldwide availability patterns.
& Ratings Patterns
Applied simple linear regression in R to analyze how global availability impacts movie ratings across diverse international content.
Feature Analytics
Analyzed 1M+ Spotify tracks in R to quantify how audio features and temporal trends drive song popularity using regression models.
Recent News & Insights
Exploring the frontiers of data science, actuarial innovation, and AI.
Building Production-Ready Agentic AI Systems
Deep research agents automating financial analysis workflows at scale.
Read full article →Mixed-Methods NLP Analysis of UN Reports
TF-IDF, NER, and lexicon-assisted qualitative coding of 500+ CRPD reports.
Read full article →Risk-Aware Modeling & AI Integration
Bridging classical actuarial science with machine learning for risk prediction.
Read full article →