Data Scientist - Credit Risk, Growth and Fraud Models

About the role

A Data Scientist is needed in Lagos to build and run models such as credit risk scoring, propensity and growth models, and fraud and anomaly detection, embedded directly into lending and wallet products. The role works alongside the Head of Data, Financial Services to turn platform data from distributors and retailers into data-led decisions, covering model selection, deployment, continuous monitoring and adoption.

Key responsibilities

• Credit risk: build, validate and maintain scorecards, PD and DPD roll-rate models, score distributors and retailers (including thin-file customers), recommend limits and pricing, and feed early warning signals
• Growth: build propensity, segmentation and next-best-offer models, churn/dormancy models and A/B test experiments
• Fraud and anomaly detection: models for onboarding, transactions and repayments, tuned alerts combining rules and machine learning
• Embed models in live decision flows (loan approval, limits, offers, fraud checks) as scores, APIs or batch outputs
• Monitor performance, data drift and population stability (Gini, KS, PSI), set retraining triggers and keep documentation and governance
• Automate scoring runs and decision rules, build feature pipelines with data engineering, and explain model results to credit, product, sales and leadership teams

Required profile

• Bachelor's or Master's in Statistics, Mathematics, Economics, Computer Science, Data Science or a related field
• 3-6 years of hands-on data science experience, at least 2 in fintech, digital lending, banking or payments
• Proven credit risk modelling (scorecards, PD, DPD/roll-rate) on real transaction data, preferably for SME, merchant or thin-file lending
• Propensity, segmentation or churn models that drove growth, and fraud/anomaly detection on transaction or payment data
• At least one model taken from idea to production and monitored after launch
• Solid grasp of financial terms (NPA, DPD, PD/LGD/EAD, vintage analysis, CASA)

Required skills

• Strong Python and SQL; PySpark or similar big-data tools
• Snowflake or a similar cloud data warehouse
• Logistic regression, scorecards (WoE/IV), gradient boosting (XGBoost, LightGBM), clustering, survival models
• Fraud: isolation forest, autoencoders, graph features, rule-plus-ML hybrids
• Explainability (SHAP, reason codes, feature importance); MLOps deployment, versioning, drift monitoring (PSI, CSI)
• Power BI or similar BI tool

What we offer

• Monthly salary of USD 1,500-3,000
• Local allowance of USD 400 equivalent in local currency
• Accommodation and company-provided car
• Immigration expenses covered, plus a one-time travel expense to India for a 30-calendar-day leave