Key Responsibilities: • Work with large datasets to identify patterns, trends, and anomalies that may indicate fraudulent activity • Utilize data analytics tools and methodologies to conduct in-depth assessments and generate Fraud rules and reports on fraud trends (including first-party and third-party fraud). • Collaborate with cross-functional teams, including risk management, operations, and compliance, to enhance fraud prevention measures. • Monitor industry trends, regulatory changes, and best practices to continually enhance fraud prevention strategies.

Technical Skills Needed: • 8+ years of experience in Python coding, SQL, Machine Learning • Understanding of Deep Learning / Neural Networks (ANNs) • Hands-on experience with XGBoost, Random Forest, GBM • Experience in end-to-end ML model development: o Data Preparation o Feature Engineering o Model Training & Evaluation o Model Deployment • Positive understanding of: o Imbalanced data handling o Model evaluation metrics o Model explainability • Exposure to LLMs/RAG concepts • Cloud exposure is valuable to have: AWS/GCP/Azure • Ops tools experience is good to have : Docker/Airflow/Spark/ML Ops • BFSI domain is valuable to have, not mandatory

📌 Data Science Manager Bengaluru
🏢 Straive
📍 Bengaluru