LUCKY AKOKO

Available to hire

Work Experience

Junior Data Scientist at Nolmoat Inc
November 1, 2023 - December 1, 2025
Designed and deployed machine learning models to predict customer behavior and analyze churn. Built and maintained automated data pipelines using Python and SQL, reducing manual processing time and ensuring reliable data flows. Developed interactive dashboards in Tableau and Power BI to monitor KPIs and support decision-making across departments. Applied NLP, time series analysis, and classification algorithms to extract insights from structured and temporal data sources. Collaborated with cross-functional teams including engineers and product managers to identify data-driven improvements. Presented findings to both technical and non-technical stakeholders, translating complex data into clear, actionable recommendations.

Education

Master's Degree in Financial Engineering (Ongoing) at WorldQuant University
January 11, 2026 - July 20, 2027
BSc. Actuarial Science at Southeastern Kenya University
January 1, 2013 - December 31, 2016
Graduate Certificate in Data Science at World Quant University
July 1, 2021 - July 20, 2022

Qualifications

WorldQuant University - Master’s Degree in Financial Engineering (Ongoing)
January 11, 2026 - July 20, 2027
World Quant University - Graduate Certificate in Data Science
July 1, 2021 - July 20, 2022

Industry Experience

Financial Services, Professional Services, Education
    Optimizing Credit Risk: Maximizing Approvals While Minimizing Defaults

    I am excited to share the results of my latest project, which is an end-to-end Machine Learning pipeline engineered to automate and optimize credit risk assessment.
    Manual credit scoring is often slow, vulnerable to human bias, and struggles with non-linear financial indicators. To solve this, I developed a data-driven framework using an advanced XGBoost Classifier to evaluate borrower risk dynamically.
    Key technical highlights from the project:
    Engineered Impactful Features: Synthesized key metrics like Debt-to-Income (DTI), Loan-to-Income, and Experience ratios to capture true repayment headroom.
    Class Imbalance & Leakage Protection:
    Integrated a robust ColumnTransformer preprocessing pipeline alongside algorithmic weight scaling to ensure clean out-of-sample generalization.
    Risk-Averse Threshold Tuning:
    Shifted the classification boundary to a strict 70% confidence cutoff, significantly reducing False Positives and protecting institutional portfolio capital.