Lead Data Scientist

Key Responsibilities

Model Development

• Design, train, validate, and deploy ML models for use cases such as: real-time transaction fraud detection, card-not-present (CNP) risk scoring, authorization decline/approval optimization, chargeback/dispute prediction, merchant risk scoring, and AML/transaction-monitoring anomaly detection.
• Engineer features from transactional, behavioral, and device/network data while respecting strict latency budgets (often sub-100ms scoring at authorization time).
• Evaluate and select appropriate techniques - gradient boosting, deep learning, graph-based fraud detection, anomaly detection, time-series methods, based on the problem, not fashion.

Productionization & MLOps

• Work with Engineering to deploy models into real-time and batch pipelines, ensuring reliability, monitoring, and rollback safety in a payments-critical path.
• Build and maintain model monitoring for drift, data quality, and performance degradation, with alerting tied to operational and fraud-loss KPIs.
• Contribute to (or help establish) MLOps practices: versioning of models/features/data, reproducible training pipelines, CI/CD for models, and A/B or shadow-testing frameworks before full rollout.

AI/LLM-Adjacent Work

• Apply modern AI tooling - including LLMs such as Claude - to accelerate data science workflows: automated feature exploration, model documentation, anomaly narrative generation for fraud analysts, and code generation/review for data pipelines.
• Explore applied use cases for LLMs in risk and compliance narratives (e.g., summarizing suspicious activity patterns for SAR drafting support, with mandatory human review).

Cross-Functional Collaboration & Governance

• Partner with Fraud & Risk and Compliance teams to ensure model outputs are explainable and defensible to auditors, regulators, and card scheme risk teams (Visa, Mastercard).
• Present findings and model performance to non-technical stakeholders (Risk Committee, Product, Client Services) in clear, decision-useful terms.
• Ensure all data handling complies with PCI DSS, data residency requirements, and internal data governance policies - particularly around cardholder data (PANs, CVVs, authentication data).

Required Qualifications: -

8 to 12 years of overall experience, with 3+ years specifically as a Data Scientist or ML Engineer. - Strong domain expertise in Fintech, Payments, or Banking is highly preferred. - High proficiency in Python (pandas, scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow) and SQL. - Demonstrated experience deploying production-grade models (not just exploratory research or notebooks). - Experience with cloud infrastructure (AWS/GCP/Azure - SageMaker, Vertex AI, Databricks) and streaming data architectures (Kafka, Flink).