AI/ML Data Scientist

Role overview

Work location

• The work location is hybrid remote in Bangalore City, Bengaluru, Karnataka.

Machine learning and model development

• Design, develop, validate, and deploy models for fraud, risk, authorization, merchant risk, chargeback, and anomaly detection.
• Build features from transactional, behavioral, device, and network data.
• Apply classification, anomaly detection, graph analytics, time-series modeling, and deep learning techniques.

MLOps and production engineering

• Deploy models in real-time and batch-processing environments.
• Implement monitoring, drift detection, performance tracking, and retraining strategies.
• Establish model versioning, CI/CD, reproducible training, and A/B testing practices.
• Work with engineering teams on scalability, reliability, and low-latency performance.

AI and generative AI

• Use Claude, GPT, and other AI or LLM technologies to enhance data science workflows.
• Develop AI-driven feature discovery, reporting, anomaly interpretation, and analyst productivity solutions.
• Explore governed generative AI use cases in risk, fraud, and compliance.

Collaboration and governance

• Partner with Fraud, Risk, Compliance, Product, and Engineering teams on explainable and auditable AI solutions.
• Present analytical insights and model performance to stakeholders and leadership.
• Ensure compliance with PCI-DSS, data privacy regulations, and enterprise data governance standards.

Required qualifications

• At least 10 years of overall IT experience, including at least 3 years in data science or machine learning roles.
• Strong Python, SQL, machine learning, statistical modeling, and anomaly detection expertise is required.
• Hands-on production deployment experience for machine learning models is required.
• Experience with a cloud platform such as AWS, Azure, or GCP is required.
• Exposure to Spark, Airflow, Databricks, or similar big data technologies is required.
• Knowledge of financial-services regulations, data security, and compliance frameworks is required.
• Excellent communication and stakeholder-management skills are required.

Preferred qualifications

• Payments, FinTech, banking, or financial-services experience is preferred.
• Knowledge of payment fraud typologies, graph-based fraud detection, or network analytics is preferred.
• Familiarity with Visa and Mastercard risk-management frameworks is preferred.
• Hands-on generative AI and LLM experience is preferred.
• Streaming-platform experience with Kafka or Flink is preferred.
• A master's or PhD in a quantitative discipline is preferred.