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Data Scientist with 4+ years in AI-powered analytics & machine learning
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Assessing your cultural and operational fit
I am a data scientist with a strong foundation in AI-powered analytics, machine learning and production-grade data platforms, delivering end-to-end analytics and ML solutions in B2B SaaS and consulting environments. Skilled at translating business needs into measurable requirements and extracting insights from complex data to support strategic decision-making and operational efficiency. I build production-ready analytics solutions, dashboards and AI features through cross-functional collaboration with Product, Engineering and business teams to drive measurable impact.
WorldQuant University
Bachelors · Applied Data Science
N/A – Present
Kiambu National Polytechnic
Diploma · Quantity Surveying
N/A – Present
edX and Harvard
Professional Certificate · Data Science
N/A – Present
LuxDevHQ
Professional Certificate · Data Science, Machine Learning and AI
N/A – Present
DreamTuners Global
Data Scientist
June 1, 2024 – Present
Nairobi, Nairobi, Kenya
DataForge Partners
Data Scientist
January 1, 2023 – May 31, 2024
Nairobi, Nairobi, Kenya
Powerful Construction Limited
Construction Data Analyst / Quantity Surveyor
September 1, 2022 – December 31, 2022
Nairobi, Nairobi, Kenya
Tribase Labs
Junior Data Analyst
September 1, 2021 – August 31, 2022
Nairobi, Nairobi, Kenya
Customer Intelligence and Market Basket Analytics
June 17, 2026 – Present
• Applied RFM segmentation to 3 years of automobile parts sales data to classify customers by recency, frequency, and monetary value, enabling targeted retention and upsell strategies. • Implemented Apriori-based Market Basket Analysis on grocery POS transactions to surface high-confidence product association rules and combo recommendations. • Delivered dual-domain analysis combining customer lifetime value insights with promotional bundling opportunities for marketing and commercial teams.
View ProjectLoan Default Prediction and Credit Risk Analytics
June 17, 2026 – Present
• Engineered and analyzed credit risk datasets containing borrower financial history, delinquency behaviour and debt indicators to identify the strongest drivers of loan default risk. • Achieved ROC-AUC score of 0.939 and improved default detection recall from 61.8% to 74% through threshold calibration, feature engineering and imbalanced learning techniques. • Designed a dual-model credit risk architecture combining high-performance predictive scoring with explainable ECOA-compliant rejection reasoning for transparent lending decisions. • Conducted financial impact and risk analysis projecting over $1M annual NPA savings potential, while delivering executive-level reporting, feature importance analysis and loan officer scorecards for operational deployment.
View ProjectSales Forecasting - Sparkling Wine
June 17, 2026 – Present
• Built time series forecasting models from scratch to predict sparkling wine sales across seasonal demand cycles. • Applied decomposition techniques to isolate trend, seasonality and residual components, improving model interpretability and forecast reliability. • Evaluated model performance using MAE and RMSE to select and deploy the best-performing approach for production planning scenarios.
Cultural Fit Analysis
The candidate's project diversity, ranging from customer intelligence and market basket analysis to sales forecasting and credit risk analytics, indicates a broad interest and adaptability to different business domains. Their experience across various companies (DataForge Partners, DreamTuners Global, Powerful Construction Limited, Tribase Labs) and roles (Data Scientist, Construction Data Analyst, Junior Data Analyst) shows a willingness to learn and contribute in different capacities. The continuous pursuit of education in Data Science, Machine Learning, and AI through multiple certificates and a Bachelor's degree demonstrates a strong commitment to professional growth and a proactive learning mindset, which are positive indicators for cultural fit in a dynamic technical environment.
Soft Skills & Operational Fit
The candidate demonstrates strong soft skills in Stakeholder Communication, Data Storytelling, Agile Collaboration, Team Leadership, and Strategic Recommendations, which are crucial for a senior Data Scientist role. Their experience in documenting methodologies, limitations, and KPI definitions indicates a focus on transparency and repeatability, aligning well with operational best practices. The ability to translate complex technical findings into actionable recommendations for implementation teams suggests good operational fit.