Investment & Data Analyst based in Nairobi, sitting at the intersection of finance, data, and tooling.
I combine CFA-level finance with Python / SQL / Power BI / Excel modeling to build models and dashboards that decision-makers actually use — from corporate & crypto analytics to forecasting and NLP on company data.
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Financial modeling & valuation
- Three-statement models, DCF, scenario & sensitivity analysis
- FAST-style, audit-friendly spreadsheets
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Business intelligence & reporting
- Power BI dashboards for sales, risk and operations
- KPI design, data modeling (DAX), stakeholder-ready layouts
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Data analysis & forecasting
- Python (pandas, NumPy, matplotlib, scikit-learn)
- Time-series forecasting, performance/risk analytics
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Data prep & feature engineering
- Cleaning & standardising company/industry datasets
- Turning text + fundamentals into model-ready features
Languages & analysis
- Python · SQL · DAX · Excel / VBA
Data & ML
- pandas · NumPy · scikit-learn
- Time-series, feature engineering, basic ML/DL & NLP
BI & visualisation
- Power BI · Tableau · Matplotlib
Finance
- Financial Modeling & Valuation
- Corporate finance, equity/credit analysis
- Portfolio & risk concepts (CFA all 3 levels completed)
Workflow
- Git & GitHub · Jupyter · ETL / data cleaning
- Documentation & clear stakeholder communication
A few repos that reflect how I think about finance + data:
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Power BI Crypto Performance & Risk Dashboard
Two-page Power BI report for the top 1000 cryptocurrencies, covering market structure, performance heatmaps, volatility, liquidity and tokenomics (supply utilisation & Volume/MarketCap). -
Blu Containers – Three-Statement Financial Model
Fully linked three-statement Excel model for a fictitious eco-manufacturing company. Driver-based assumptions, ratios, scenarios and investor-style recommendations. -
Corporate Finance Model Using FAST
FAST-standard corporate finance model for a listed Kenyan tobacco manufacturer, with integrated statements, valuation outputs and scenario analysis for equity/credit use cases. -
Retail Sales Performance & Forecasting
12-month retail sales case: Pareto analysis, pricing/discount behaviour, time-of-day patterns and baseline time-series forecasting for revenue. -
Financial Time-Series Forecasting with ML & DL
Experiments comparing simple baselines vs machine/deep learning models on financial time series, focusing on when extra model complexity actually adds value. -
Company & Industry Data Pipeline
End-to-end pipeline to clean, join and feature-engineer company + industry fundamentals as a base layer for research, dashboards and models. -
NLP on Company Text Data
Applying NLP techniques to company and industry descriptions to create text-based features for screening, clustering and risk analysis on top of the fundamentals layer. -
Portfolio Website
Static site that pulls these projects together and tells the story of how I use finance + data to support real-world decisions.
- 🌍 Portfolio: kimuyu-charles.github.io/Portfolio-Website
- 💼 LinkedIn: linkedin.com/in/charles-kimuyu
- 📧 Email: charlesnzioka1@gmail.com
- Equity & credit research augmented with Python + Power BI
- Forecasting & scenario analysis for real-world operators (CFOs, PMs, founders)
- Turning messy operational data into clean, decision-ready insight
If you’d like to chat about finance, data, dashboards or modeling standards, feel free to reach out or open an issue on any repo.