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.

- Robust 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.

📌 Data Science Lead (Bengaluru)
🏢 ALTIMETRIK
📍 Bengaluru