Data Scientist Fraud Analytics & Machine Learning

Job Title: Data Scientist Fraud Analytics & Machine Learning Location: Hyderabad Experience: 6+ Years Employment Type: Full-Time Job Summary We are seeking an experienced Data Scientist with strong expertise in Machine Learning, Fraud Analytics, Fraud Strategy, and Model Development to join our data science team. The ideal candidate will have a proven track record of building and deploying multiple machine learning models in an industry environment, with hands-on experience taking models from experimentation through production. Experience working within highly regulated industries and navigating Model Risk Management and Validation (MRMV) processes is highly desirable. The candidate will work closely with business, fraud strategy, data engineering, technology, risk, and model governance teams to develop data-driven solutions that identify and prevent fraudulent activities while maintaining strong model governance and regulatory compliance. Key Responsibilities Machine Learning Model Development Design, develop, validate, and deploy machine learning models to solve complex business problems.Demonstrate experience building and deploying at least 5 ML models in an industry setting.Develop predictive models for fraud detection, risk assessment, transaction monitoring, and anomaly detection.Perform feature engineering, model selection, hyperparameter tuning, and model optimization.Evaluate model performance using appropriate statistical and machine learning metrics.Translate analytical findings into actionable business recommendations.Fraud Analytics & Modeling Develop advanced analytics and machine learning solutions to identify fraudulent transactions and suspicious behavior.Analyze transaction, customer, behavioral, and historical data to identify fraud patterns.Develop fraud detection strategies using statistical and machine learning techniques.Identify emerging fraud trends and recommend appropriate detection strategies.Work with Fraud Strategy teams to translate business rules and fraud patterns into analytical models.Optimize fraud models to balance fraud detection, false positives, customer experience, and operational costs.Support development of real-time and batch fraud detection solutions.Fraud Strategy Partner with Fraud Strategy and Risk teams to understand business objectives and fraud challenges.Analyze existing fraud strategies and identify opportunities for improvement.Develop data-driven recommendations to enhance fraud detection and prevention.Evaluate the effectiveness of existing fraud rules and machine learning models.Support champion/challenger strategies and model performance comparisons.Monitor fraud trends and recommend changes to strategies based on emerging patterns.Model Development & Deployment Take ML models through the complete lifecycle from development to production deployment.Collaborate with Data Engineers and ML Engineers to productionize models.Develop scalable model scoring and inference solutions.Support model implementation across production environments.Monitor deployed models and identify performance degradation.Participate in model retraining and enhancement initiatives.Model Risk Management & Validation Work within established Model Risk Management and Validation (MRMV) frameworks.Prepare documentation required for model governance and validation.Partner with Model Risk, Validation, Risk Management, and Compliance teams.Support model validation and independent review activities.Address model validation findings and implement remediation plans.Maintain documentation covering:Model methodologyData sourcesFeature engineeringModel assumptionsPerformance metricsLimitationsMonitoring methodologyEnsure models meet organizational risk and governance standards.Regulated Industry Experience Work effectively within highly regulated environments.Ensure analytical solutions comply with applicable regulatory and organizational requirements.Support audit and regulatory reviews related to machine learning models.Maintain strong documentation and traceability throughout the model lifecycle.Understand the importance of explainability, transparency, fairness, and model risk management.Data Analysis & Feature Engineering Analyze large and complex datasets to identify patterns and relationships.Perform exploratory data analysis and statistical analysis.Develop meaningful features for fraud and risk models.Handle missing data, outliers, class imbalance, and noisy datasets.Work with structured and transactional data.Perform feature selection and dimensionality reduction where appropriate.Model Performance & Monitoring Define and track appropriate model performance metrics.Monitor model stability and predictive performance in production.Analyze model drift and changes in fraud patterns.Develop model monitoring strategies and performance reports.Identify opportunities for model recalibration and enhancement.Cross-Functional Collaboration Collaborate with: Fraud .