AI/ML Data Scientist in Bangalore, भारत

Job Description : We are looking for an experienced AI/ML Data Scientist to join our team and work on high-impact use cases across payments, fraud detection, risk analytics, and financial services. The ideal candidate should have strong hands-on experience in developing and deploying machine learning models in production environments, preferably within payments, fintech, banking, or other high-volume transactional https://jobeax.com/link/cZ8zpMXwj45ivho4 Responsibilities : Model Development : - Design, develop, train, validate, and deploy machine learning models for real-time transaction fraud detection, card-not-present (CNP) risk scoring, authorization decline/approval optimization, chargeback and dispute prediction, merchant risk scoring, and AML/transaction-monitoring anomaly detection.- Engineer and optimize features using transactional, behavioral, device, and network data while meeting strict latency requirements for real-time scoring.- Evaluate and select appropriate machine learning techniques based on business requirements, including gradient boosting, deep learning, graph-based fraud detection, anomaly detection, and time-series modeling.- Work with large and complex datasets to identify patterns, fraud signals, and opportunities for improving risk https://jobeax.com/link/xcAaea1uLy4UZUKQ and MLOps : - Collaborate with Engineering teams to deploy ML models into real-time and batch processing pipelines.- Ensure production models meet requirements for reliability, scalability, latency, monitoring, and rollback safety.- Develop and maintain monitoring frameworks for model drift, data quality, performance degradation, and fraud-loss KPIs.- Contribute to MLOps practices including model, feature, and data versioning, reproducible training pipelines, CI/CD, model validation, and retraining processes.- Implement A/B testing, shadow testing, and controlled model rollout https://jobeax.com/link/RzyoUZeA3Vxnknz4 and LLM Applications : - Apply modern AI and LLM technologies such as Claude and GPT to accelerate data science and analytics workflows.- Explore LLM-based solutions for automated feature exploration, model documentation, anomaly narrative generation, and data pipeline code generation/review.- Develop applied AI use cases for fraud, risk, and compliance workflows, including summarization of suspicious activity patterns for analyst review and SAR drafting support with appropriate human oversight.Cross-Functional Collaboration and Governance : - Collaborate with Fraud, Risk, Compliance, Engineering, Product, and Client Services teams to develop effective and explainable ML solutions.- Ensure model outputs are explainable, auditable, and defensible for internal stakeholders, auditors, regulators, and card scheme risk teams.- Present model performance, analytical findings, and business trade-offs to technical and non-technical stakeholders.- Ensure data processing and model development comply with PCI DSS, data residency requirements, security standards, and internal data governance https://jobeax.com/link/2UXmU2OWSFsZbq6t Qualifications : - 8+ years of overall professional experience, including at least 3+ years of hands-on experience as a Data Scientist or ML Engineer.- Experience in payments, fintech, banking, financial services, fraud detection, risk analytics, or other high-volume transactional environments is preferred.- Strong programming skills in Python and SQL.- Hands-on experience with Pandas, Scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.- Strong understanding of classification, anomaly detection, imbalanced datasets, and rare-event modeling.- Proven experience developing and deploying machine learning models into production environments.- Experience with model monitoring, performance tracking, drift detection, and model retraining.- Hands-on experience with cloud-based data and ML platforms such as AWS, GCP, or Azure.- Experience with platforms/tools such as SageMaker, Vertex AI, or Databricks.- Knowledge of data engineering technologies such as Spark, Airflow, or similar tools.- Understanding of financial services data security, PCI DSS, data minimization, access controls, and regulatory requirements.- Strong communication skills with the ability to explain model performance, precision/recall, false-positive costs, latency, and other ML trade-offs to business https://jobeax.com/link/FfNN38MjANEx2m9w Qualifications : - Experience with payments-specific fraud typologies such as CNP fraud, account takeover, first-party fraud, synthetic identity, BIN attacks, and card testing.- Experience with graph-based or network analysis techniques for fraud ring and merchant collusion detection.- Familiarity with card scheme rules and risk parameters, including Visa and Mastercard fraud/risk attributes.- Experience applying LLMs such as Claude or GPT to data science, analytics, feature engineering, automated EDA, reporting, or analyst-facing applications.- Experience with real-time streaming technologies such as Kafka and Flink.- Experience working with low-latency ML scoring systems.- MSc, PhD, or equivalent practical experience in Statistics, Computer Science, Applied Mathematics, Physics, Operations Research, or another quantitative https://jobeax.com/link/rBp58Vzf3FS2gkHJ Skills : Python, SQL, Machine Learning, Data Science, Fraud Detection, Payments, Risk Analytics, XGBoost, LightGBM, PyTorch, TensorFlow, Scikit-learn, MLOps, AWS, GCP, Azure, Databricks, Spark, Airflow, Kafka, Flink, Graph Analytics, Anomaly Detection, Deep Learning, LLM, GenAICandidate Profile : The ideal candidate will be a hands-on AI/ML professional with strong production experience, excellent problem-solving skills, and exposure to fraud, payments, risk, or financial services. The candidate should be comfortable working with large-scale transactional data, building real-time ML solutions, collaborating with cross-functional teams, and translating complex analytical results into actionable business insights. (ref:hirist.tech)