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 methodology
- Data sources
- Feature engineering
- Model assumptions
- Performance metrics
- Limitations
- Monitoring methodology
- Ensure 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 Strategy Teams
- Fraud Operations
- Risk Management
- Model Risk Management
- Model Validation
- Data Engineering
- ML Engineering
- Technology
- Product Management
- Compliance and Audit

Translate complex analytical concepts into clear business recommendations for technical and non-technical stakeholders.

Required Technical Skills

Machine Learning

- Strong understanding of machine learning algorithms and methodologies.
- Experience with:
- Classification
- Regression
- Clustering
- Anomaly Detection
- Ensemble Methods
- Gradient Boosting
- Decision Trees
- Experience with model selection and hyperparameter optimization.
- Strong understanding of model evaluation and validation techniques.

Fraud Analytics Strong experience in one or more:

- Fraud Detection
- Fraud Analytics
- Fraud Strategy
- Transaction Monitoring
- Payment Fraud
- Financial Crime Analytics
- Anomaly Detection
- Risk Modeling

Programming

- Strong proficiency in Python .
- Experience with:
- Pandas
- NumPy
- Scikit-learn
- XGBoost / LightGBM
- Strong SQL skills for data extraction and analysis.

Statistical & Analytical Skills

- Strong foundation in statistics and probability.
- Hypothesis testing.
- Regression analysis.




- Statistical modeling.
- Sampling techniques.
- Model performance evaluation.
- Understanding of imbalanced datasets and appropriate evaluation techniques.

Model Governance

- Hands-on experience with Model Risk Management and Validation (MRMV) .
- Understanding of model lifecycle governance.
- Model documentation and validation processes.
- Model performance monitoring.
- Model risk assessment.
- Experience working with governance and compliance teams.

Model Evaluation Metrics Experience working with appropriate ML and fraud analytics metrics such as:

- Precision
- Recall
- F1 Score
- ROC-AUC
- PR-AUC
- Confusion Matrix
- False Positive Rate
- False Negative Rate
- KS Statistic
- Population Stability Index (PSI)
- Model Stability Metrics

Understanding the business impact of these metrics in fraud detection environments is highly desirable. Data & Technology Skills

- SQL
- Python
- Pandas
- NumPy
- Scikit-learn
- XGBoost
- LightGBM
- Jupyter
- Git
- Data Visualization
- Tableau / Power BI (preferred)

Experience with cloud platforms such as AWS, Azure, or GCP is a plus.

Preferred Qualifications

- Experience in Banking, Financial Services, FinTech, Payments, or Insurance .
- Strong experience with fraud analytics and fraud modeling.
- Experience with payment transaction data.
- Experience developing real-time fraud detection models.
- Experience with large-scale transactional datasets.
- Exposure to MLOps and production ML deployment.
- Experience with model monitoring and model drift detection.
- Experience working with Model Risk Management, Model Validation, Risk, and Compliance teams.
- Knowledge of explainable AI and responsible AI practices.
- Master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field is preferred.

Key Deliverables

- Production-ready machine learning models.
- Fraud detection and prevention models.
- Fraud analytics and strategy recommendations.
- Feature engineering frameworks and analytical datasets.
- Model validation and governance documentation.
- Model performance and monitoring reports.
- Data-driven recommendations for improving fraud detection.
- Continuous enhancements to fraud models and strategies.

Soft Skills

- Strong analytical and problem-solving skills.
- Excellent communication and stakeholder management abilities.
- Ability to explain complex ML concepts to business and risk stakeholders.
- Strong attention to detail and documentation skills.
- Ability to work effectively in cross-functional teams.
- Strong understanding of business impact and customer experience.
- Ability to operate effectively in highly regulated environments.
- Strong ownership and ability to work independently.

Education & Experience

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative discipline .
- 6+ years of professional experience in Data Science, Machine Learning, Risk Analytics, Fraud Analytics, or a related field.
- Proven experience building and deploying 5+ machine learning models in an industry environment .
- Demonstrated experience working with Fraud Analytics / Fraud Strategy / Fraud Modeling .
- Experience navigating MRMV / Model Risk Management and Validation processes in a highly regulated industry.

📌 Data Scientist – Fraud Analytics & Machine Learning (Hyderabad)
🏢 IntraEdge
📍 Hyderabad