Position :

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+ years of overall experience, including at least 3+ years as a Data Scientist or ML Engineer, ideally in payments, fintech, banking, or another environment with high-volume transactional data and real-time decisioning.

- Strong proficiency in Python (pandas, scikit-learn, XGBoost/LightGBM, PyTorch or TensorFlow) and SQL.

- Demonstrated experience building and deploying models into production (not just research/exploratory work), with attention to monitoring and retraining considerations.

- Solid understanding of classification, anomaly detection, and imbalanced-class problems (fraud is a classic rare-event problem).

- Experience with cloud data/ML infrastructure (AWS/GCP/Azure - e.g., SageMaker, Vertex AI, Databricks) and standard data engineering tools (Spark, Airflow, or similar).

- Understanding of the regulatory and security constraints of financial services data (PCI DSS, data minimization, access controls).

- Strong communication skills - able to translate model outputs and trade-offs (precision/recall, false positive cost, latency) into business decisions for risk and product stakeholders.

Preferred Qualifications :

- Direct experience with payments-specific fraud typologies: CNP fraud, account takeover, first-party fraud, synthetic identity, BIN attacks, or card testing.

- Experience with graph-based or network analysis techniques for fraud rings/merchant collusion detection.

- Familiarity with card scheme rules and risk parameters (Visa Risk Manager, Mastercard Fraud attributes, or similar).

- Experience applying LLMs (e.g., Claude, GPT) to data science workflows - feature engineering assistance, automated EDA, report generation, or analyst-facing summarization tools.

- Exposure to real-time streaming architectures (Kafka, Flink) for low-latency scoring.

- MSc/PhD in a quantitative field (Statistics, Computer Science, Applied Math, Physics, Operations Research) or equivalent practical experience.