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Angela Gitonga

Data Analytics Specialist with 3+ years of enterprise experience at Jubilee Insurance. Transforming data into actionable insights.

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AI-Powered Data Analytics Portfolio

Angela GitongaI build analytics that ship.

Data Analytics Specialist → AI-Augmented Analytics Leader

I'm a Data Analytics Specialist at Jubilee Insurance Kenya. I turn messy data into decisions people actually act on. The sweet spot is where traditional analytics meets AI—and that's where I build. niko kenya

See What I BuiltChat with My AIWatch the Architecture Reel
Why I'm excited about this role

Kwa nini hapa

We're at an inflection point in analytics. The next decade belongs to analysts who can build, not just report.

Spot the Leverage

Find the exact workflow where AI saves real time

Ship the Solution

Practical builds that people actually use

Translate the Value

From model outputs to executive outcomes

Make It Usable

So non-technical teams can move fast too

This is the work I want to do. niko tayari — and I've already started.

What I built to prove it

Ninachojenga

I didn't want to just say I'm into AI. I built it, tested it, and shipped it.

Executive Dashboard Generator

60-70% faster dashboard delivery

The Problem

Executive dashboards still take days of back-and-forth — data profiling, KPI debates, visualization choices, then narrative clean-up.

My Solution

  • • Accepts any business dataset (CSV/Excel upload)
  • • Automatically profiles data and identifies dimensions vs. measures
  • • Recommends relevant KPIs with business context
Next.jsConvexClaude APIOpenAIRecharts
Open Live Demo

Portfolio AI Assistant

Always-on first interview

The Problem

Recruiters have limited time. They want fast, specific answers without scanning a whole portfolio.

My Solution

  • • AI chatbot trained on professional background
  • • Answers questions about skills, projects, experience
  • • Provides specific metrics and examples
Next.jsConvexClaude APIStreamingReal-time
Open Live Demo

Insurance Claims AI Analyzer

Built on real fraud prevention wins

The Problem

Claims analysis is slow and inconsistent — two analysts can score the same claim differently.

My Solution

  • • Analyzes individual claims or batch uploads
  • • Provides risk scores (1-10) with explainable reasoning
  • • Calculates fraud probability with specific red flags
Next.jsConvexClaude APIOpenAIBatch Processing
Open Live Demo
Production-ready architecture

Muundo Imara

Interactive architecture visualization: provider switching first, system flow second.

Interactive control desk

Scrub the story like a film editor

Five scenes, one reel: intro, provider routing, architecture flow, impact metrics, and outro. Built to showcase visual thinking.

Jump points

This reel runs live in the browser - no GIFs, no shortcuts.

Provider switchboard
  • Claude + GPT-4o routing without vendor lock-in
  • Task-based model selection, not hard-coded
  • Real-time switching for experimentation
Real-time core
  • Convex for sync + streaming responses
  • Clerk for secure, production auth
  • Responsive by default — desktop to mobile
Clean, intentional code
  • TypeScript everywhere, predictable types
  • Prompt system separated for fast iteration
  • Modular components that scale past demos
Track record, delivered

Rekodi yangu

Real-world impact at Jubilee Insurance Kenya.

0+

Executive Dashboards

Serving 200+ decision-makers across the C-suite

0+

Hours Saved Annually

Automation that clears recurring reporting debt

KES 0.0M

Fraud Prevented

Analytics-driven fraud detection wins

0%

Reduction in Requests

Self-service analytics adoption at scale

Data Visualization & BI
  • • Power BI (Expert - 4 years)
  • • Tableau (Advanced)
  • • Excel (Expert with VBA)
Programming & Data
  • • SQL (Advanced)
  • • Python (Intermediate)
  • • pandas, numpy, matplotlib
Automation & Engineering
  • • Power Automate (Advanced)
  • • ETL Processes
  • • Azure Data Factory (Basic)
AI & New Skills
  • • Prompt Engineering
  • • AI Application Development
  • • Streaming Responses
  • • RAG Concepts
My vision for AI in analytics

Maono yangu

I see three waves of adoption in data teams — and I want to build Wave 2.

Wave 1Now
AI as a Tool for Analysts
  • Copilots for code generation
  • AI-assisted documentation
  • Automated data profiling
Wave 2Next 1-2 years
AI-Augmented Workflows
  • Automated dashboard generation
  • AI-powered anomaly detection
  • Natural language to SQL
Wave 33-5 years
AI-Native Analytics
  • Autonomous reporting agents
  • Proactive insights delivery
  • Self-optimizing pipelines

I want to build Wave 2 and set the runway for Wave 3. hiyo ndio kazi

What I bring to an AI team

Ninaleta nini

Domain expertise, technical foundation, and a builder mentality.

Domain Expertise

Insurance, finance, and business analytics — I spot high-impact AI use cases fast.

Technical Foundation

SQL, Python, data modeling — I build, not just ideate.

Builder Mentality

I see a gap and ship a fix. No waiting around.

Communication Skills

I translate AI capability into business outcomes executives trust.

Learning Velocity

Self-taught to build AI apps in days. I move quickly.

Questions I'd love to discuss

Maswali ya maana

Let's talk about the future of AI in analytics.

1

Where do you want AI to make analysts 10x faster first?

2

Which pain point keeps repeating in your analytics workflow?

3

What is your build vs. buy line, and where are you open to experiments?

4

In year one, what outcome makes you say, "this worked"?

5

How do you want responsible AI handled — guardrails, reviews, or both?

Twende kaziLet's build something that actually ships.

I'm not just looking for a job. I'm looking for a team that wants to ship real AI tools, not slide decks.

Imagine every analyst having an AI assistant that builds dashboards 10x faster. Every executive getting proactive insights. Every decision backed by AI-augmented analysis.

This is the future I want to help build. niko tayari.

Get in TouchChat with My AI

“The best way to predict the future is to build it.”

— Angela Gitonga