Job Overview
Position: Lead Data Scientist – Embedded Finance & Credit Scoring (Remote, Qatar)
Location: Remote (Qatar-based mandate)
Type: Full-Time | Remote
Industry: Fintech – Embedded Lending / Alternative Credit Scoring
Experience Level: 7+ years (Minimum 2 in embedded finance or credit modeling)
Position Summary
The embedded finance wave is reaching a strategic inflection point in Qatar, where regulators and tech platforms are preparing for an era of contextual financial services delivered directly at the point of interaction. This forward-looking remote opportunity is for a Lead Data Scientist who will craft and deploy data-driven credit scoring models using alternative data sources, behavioral signals, and cross-platform engagement patterns to power embedded micro-lending and merchant finance across the Qatari market and broader MENA region.
You will be instrumental in designing ethical, explainable AI systems that balance credit inclusion with institutional-grade risk governance.
Core Responsibilities
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Build alternative credit scoring models using telecom usage, mobile behavior, location data, and e-commerce engagement metrics.
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Work with engineers to productionize machine learning models via RESTful APIs and real-time scoring pipelines.
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Collaborate with embedded finance teams on Buy Now Pay Later (BNPL), micro-credit, and working capital tools.
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Ensure that models comply with QCB regulations and ethical AI frameworks related to discrimination, fairness, and interpretability.
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Drive experiments for loan default prediction, churn reduction, and fraud anomaly detection.
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Interface with product managers to map scoring outcomes to user experience flows (e.g., onboarding, pre-approval).
Requirements
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Strong statistical and machine learning foundation – supervised learning, boosting models, ensemble methods.
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Familiarity with feature engineering for behavioral and transactional data.
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Prior experience in lending, financial inclusion, or embedded finance models.
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Ability to work asynchronously across distributed remote teams in multiple time zones.
Preferred Tools & Stacks
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Python (pandas, sklearn, xgboost), Spark, Jupyter, MLflow.
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Cloud-based data pipelines (AWS Sagemaker, Azure ML, or GCP Vertex).
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Experience using credit bureau APIs and/or telecom scoring data a major plus.
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