Turning Data Into Actionable Insights

Investment & Data Analyst | Python | SQL | Excel | Financial Modeling | Power BI

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About Me

I'm Charles Kimuyu, an Investment & Data Analyst based in Nairobi. I sit at the intersection of finance and analytics. I combine CFA-level finance training with hands-on Python/SQL/Power BI to build models and dashboards that decision-makers actually use: I’m as comfortable building a three-statement model or valuing a company as I am writing Python code, shaping data in SQL, or designing a Power BI dashboard.

By day, I work as a Data Analytics Coordinator at Kenya Builders & Concrete, where I’ve automated reporting in Power BI, improved data quality, and helped management track margins, costs, and operational KPIs in near real time. On the side, I build models and tools that blend corporate finance, markets, and data science — from crypto risk dashboards to machine-learning forecasting experiments.

Having successfully completed all 3 CFA levels and a background in Economics & Sociology, I bring strong grounding in valuation, financial statements, and portfolio thinking. My goal on every project is the same: turn noisy data into clear, decision-ready insight that a CFO, PM, or founder can actually use.

What I Do

I help decision-makers bridge the gap between finance and data: from classic valuation and budgeting questions to dashboards and models that track risk, performance and opportunities in real time.

Investment & Corporate Finance Analysis

Build and maintain three-statement and DCF models, run scenario and sensitivity analysis, and assess value drivers and risk in corporate and market contexts.

Data & Business Intelligence

Design and automate Power BI dashboards and Excel reports, clean and shape data with SQL/Python, and deliver clear KPIs and insights that management can act on.

Advanced Analytics & Forecasting

Experiment with time-series models, machine learning and NLP on financial and business data to test hypotheses, improve forecasts, and uncover patterns that aren’t visible in topline summaries.

Equity Research & Report Writing

Apply an equity research framework to companies and sectors: structuring the investment case, analysing fundamentals, and writing concise, decision-ready reports aligned with CFA-level standards.

Technical Skills

Core tools & strengths: Equity Research & Report Writing · Financial Modeling (3-statement & DCF) · Power BI · Python · SQL · Time-series Forecasting · Machine Learning · NLP · Excel/VBA · Git & GitHub

Equity Research & Report Writing

Equity research process, investment thesis development, structured report writing and communication in line with CFA practice.

Financial Modeling

Three-statement models, DCF, scenario and sensitivity analysis, FAST-style spreadsheets and KPI dashboards.

Business Intelligence

Power BI (DAX, data modeling, dashboards), Tableau, report design and stakeholder-focused storytelling.

Python & Notebooks

Pandas, NumPy, Matplotlib, Seaborn, scikit-learn and Jupyter for analysis, modeling and reproducible workflows.

Databases & SQL

Query design, joins, aggregations, window functions and performance-minded analytical queries for reporting and models.

Machine Learning & Forecasting

Supervised models, time-series forecasting experiments, model evaluation and feature engineering for financial and business data.

Natural Language Processing

Text cleaning, tokenisation, feature extraction (e.g. bag-of-words / TF-IDF-style) and exploratory analysis of company and industry text.

Tools & Workflow

Excel/VBA, Git & GitHub, data cleaning/ETL, documentation and clear communication of results to technical and non-technical audiences.

Selected Projects

I work at the intersection of investment analysis and data science — from corporate and crypto valuation models to Python-based forecasting, NLP on company text, and Power BI dashboards for market and risk analytics.

Crypto Performance & Risk Dashboard in Power BI

Power BI DAX Data Modeling Data Visualization Crypto Analytics

Designed a two-page Power BI dashboard analyzing the top 1000 cryptocurrencies across market structure, performance, volatility, liquidity, and tokenomics.

Key Features: Market overview with market-cap distribution by tier, Top-10 vs others, and size-vs-volume scatter; Performance & Risk Explorer with timeframe heatmap, return/volatility/drawdown distributions, and synced slicers for rank, tier, and symbol. Tokenomics module tracks supply utilization and Volume-to-Market-Cap and highlights coins with extreme circulating supply profiles.

Business Impact: Turns raw market data into an actionable risk-and-opportunity map, enabling faster crypto research and helping users spot liquid movers, deep drawdowns, and projects with unusual tokenomics for deeper due diligence.

View Project

Blu Containers Financial Model

Excel VBA Financial Analysis DCF Modeling Scenario Planning

Built a fully linked three-statement financial model for Blu Containers, a fictitious manufacturer of eco-friendly water storage tanks made from recycled materials.

Key Features: Integrated Income Statement, Balance Sheet and Cash Flow with automated drivers, DCF valuation, bull/base/bear scenarios and Monte Carlo simulation. Includes working-capital metrics (DSO, inventory turns), KPI dashboards and variance analysis comparing actuals vs projections.

Business Impact: Provides investors and management with a structured way to test growth, margin and capex assumptions, quantify valuation ranges and understand the key drivers of returns for a sustainability-focused manufacturing business.

View Project

Corporate Finance Model Using FAST Standards

Excel Financial Modeling FAST Standards Valuation Corporate Finance

Built a corporate finance model for a listed tobacco manufacturer in Kenya, structured using the FAST (Flexible, Appropriate, Structured, Transparent) financial modeling standards.

Key Features: Integrated historical financials, operating drivers, and funding assumptions into an integrated three-statement model with valuation outputs (enterprise value, equity value) and scenario analysis for key revenue and margin drivers.

Business Impact: Provides a transparent, audit-friendly model that can be used for valuation, credit analysis, and strategy discussions, with a structure that can be reused for other corporate finance case studies.

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Financial Time-Series Forecasting ML & DL

Python Jupyter Time Series Machine Learning Deep Learning

Experiments with forecasting financial time series using a mix of traditional models and modern ML/DL architectures, comparing their ability to capture trends, seasonality, and regime changes.

Key Features: Implements baselines (moving average / AR-type models) alongside machine learning approaches and recurrent neural networks, with a consistent evaluation framework for train/test splits and forecast accuracy.

Business Impact: Demonstrates how more advanced models can augment classic forecasting techniques for markets and financial KPIs, highlighting where extra model complexity genuinely adds value versus where simpler models are sufficient.

View Notebook

Retail Sales Performance Forecasting

Python Pandas NumPy Matplotlib Predictive Modeling

Explored 12 months of retail sales data (185k+ transactions) to uncover revenue drivers, customer behaviour, and seasonality, then built time-series models to forecast future sales.

Key Insights: Confirmed a Pareto pattern where ~20% of products generated the majority of revenue, identified price points where demand elasticity shifted, and showed that mid-week (Tue–Thu) daytime hours drove over half of weekly sales volume.

Business Impact: Recommended targeted promotion of high-value products, inventory rebalancing to cut holding costs, and optimized promo timing—together supporting higher conversion rates and more efficient stock planning.

View Code

Company Industry Data Pipeline

Python Pandas Data Cleaning Feature Engineering ETL

End-to-end data pipeline project that takes raw company and industry data from CSV sources through cleaning, standardisation, and feature creation ready for modeling.

Key Features: Handles missing values, type inconsistencies, and categorical encoding; engineers domain-relevant features such as size buckets, sector groupings, and ratio metrics; and structures the workflow in a reproducible Jupyter notebook.

Business Impact: Showcases the “unseen” work behind any robust model—turning messy datasets into reliable, analysis-ready tables that improve model stability and interpretability.

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NLP Company Text Analysis

Python NLP Text Processing Feature Engineering Jupyter

Applies core NLP techniques to company and industry text data, transforming unstructured descriptions into structured features for downstream analysis.

Key Features: Includes text cleaning and tokenisation, creation of numerical text representations (such as bag-of-words / TF-IDF-style features), and exploratory analysis of term usage across industries and companies.

Business Impact: Illustrates how textual disclosures and descriptions can be converted into quantitative signals, opening the door to richer screening, clustering, or risk-scoring models in fundamental and credit analysis.

View Notebook

Education

CFA Institute Logo

CFA Institute

Chartered Financial Analyst (CFA)
2025

Intensive program covering Equity Valuation, Fixed Income Valuation, Financial Statement Analysis, Corporate Finance, Derivatives, Alternative Investments, and Portfolio Management with real-world projects and case studies.

Egerton University Logo

Egerton University

Bachelor of Arts in Economics & Sociology
Graduated: 2019

Relevant coursework: Statistics and Econometrics, Macro and Micro Economics, Development Economics, Research Methodology, Sociology of Development & Organization, Sociology of Gender, Poverty & Rural Studies and Data Analysis.

Certifications

Python for Data Science

Moringa School / DataCamp
2023

Data Visualization with Power BI

Microsoft / CFI
2024

Financial Modeling & Valuation

CFI / Wall Street Prep
2024

Let's Connect

I'm always interested in hearing about new opportunities, collaborations, and challenging finance & data problems.

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charlesnzioka1@gmail.com
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LinkedIn Profile
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GitHub: @Kimuyu-Charles