Straive - Senior Data Scientist - Fraud Risk

Senior Data Scientist

Experience: 4 - 6 Years

Location: Bengaluru / Hyderabad / Gurugram

Notice Period: 30 - 40 Days; Immediate Joiners Preferred

Domain Preference: Banking / Payments

Job Summary:

We are looking for an experienced Senior Data Scientist - Fraud Risk to design, develop, and deploy advanced Machine Learning solutions for detecting and preventing transactional and payment fraud at scale.

The ideal candidate should have strong expertise in Machine Learning and Python, along with experience working with complex transactional data.

Candidates with prior experience in banking, payments, fintech, or risk analytics will be preferred.

Key Responsibilities:

• Design, develop, train, and deploy Machine Learning models for transactional and payment fraud detection.
• Analyze large and complex transactional datasets to identify fraud patterns, anomalies, and emerging risks.
• Develop predictive models and risk-scoring solutions to improve fraud detection and prevention.
• Optimize models to reduce false positives while maintaining high fraud detection accuracy.
• Perform data exploration, feature engineering, model validation, and performance monitoring.
• Collaborate with engineering teams to integrate and deploy ML models into production environments.
• Partner with Product, Risk, Engineering, and Business teams to understand fraud-related challenges and develop data-driven solutions.
• Monitor model performance and continuously improve models based on changing fraud patterns and business requirements.
• Contribute to the development of scalable and robust fraud risk analytics frameworks.
• Present analytical findings, model performance, and recommendations to relevant stakeholders.

Required Skills & Experience:

• 4 - 6 years of experience in Data Science, Machine Learning, or a related field.
• At least 1+ year of hands-on experience with Machine Learning.
• At least 1+ year of experience with Python programming.
• Strong understanding of Machine Learning algorithms and statistical modelling.
• Experience working with large-scale and complex datasets.
• Strong analytical and problem-solving skills.
• Experience with data preprocessing, feature engineering, model training, and evaluation.
• Understanding of model performance metrics and techniques for handling imbalanced datasets.
• Ability to translate business problems into effective ML solutions.

Preferred Skills:

• Prior experience in Banking, Payments, FinTech, or Fraud / Risk Analytics.
• Experience building fraud detection, transaction monitoring, risk scoring, or anomaly detection models.
• Knowledge of payment transaction data and fraud typologies.
• Experience deploying and monitoring ML models in production.
• Familiarity with cloud-based ML platforms and MLOps practices will be an advantage.