Data Scientist

## About the role We are seeking a highly skilled Data Scientist with strong expertise in fraud detection and financial crime analytics. The ideal candidate will be responsible for delivering end-to-end machine learning solutions, from problem formulation to model deployment, to detect anomalies, prevent fraud, and strengthen risk management frameworks. ## Responsibilities - Develop and implement machine learning models for fraud detection, risk scoring, and anomaly detection - Work on end-to-end ML lifecycle including data collection, feature engineering, model training, validation, and deployment - Apply advanced algorithms such as XGBoost, Random Forest, and other ensemble models for predictive analytics - Analyze large and complex datasets to identify fraud patterns, suspicious behaviors, and emerging risks - Collaborate with business, risk, and compliance teams to translate requirements into scalable data science solutions - Deploy models into production and monitor performance, ensuring accuracy and stability over time - Perform model tuning, validation, and performance optimization - Build reusable, scalable ML pipelines and frameworks - Work with structured and unstructured data sources to enhance model effectiveness - Communicate insights and findings clearly to both technical and non-technical stakeholders ## Requirements - 6–9 years of experience in Data Science / Machine Learning roles - Strong proficiency in Python programming (NumPy, Pandas, Scikit-Learn, etc.) - Hands-on experience with: - Machine Learning algorithms (XGBoost, Random Forest, Gradient Boosting, etc.) - Fraud Detection / Financial Crime Analytics use cases - Experience in end-to-end ML project delivery - Strong expertise in model development, evaluation, and deployment - Solid understanding of statistics, probability, and data modeling techniques - Experience working with large-scale datasets - Knowledge of data preprocessing, feature engineering, and model explainability - Strong analytical thinking and problem-solving skills ## Nice to have - Experience in Banking / Financial Services / FinTech domain, especially fraud and risk - Familiarity with real-time fraud detection systems - Exposure to big data technologies (Spark, Hadoop) - Experience with model deployment tools (Docker, APIs, MLOps frameworks) - Knowledge of regulatory compliance and risk frameworks in financial services ## What we offer - Location: Gurgaon - Notice Period: Immediate joiners preferred (within 15 days) - Domain: Financial Crime & Fraud Analytics