Description

Lead the development and deployment of machine learning models to mitigate risk, predict fraud, and optimize payments.

This role is on-site.

Responsibilities

• Design, train, and deploy ML models for fraud prediction and merchant risk management using supervised learning and deep learning.
• Develop underwriting models to assess merchant creditworthiness and payment security.
• Build time-series forecasting models to predict losses and conduct scenario analysis for risk strategy optimization.
• Implement advanced techniques including reinforcement learning, graph-based models, and Transformer architectures for unstructured data.
• Manage data preparation and predictive modeling workflows using AWS SageMaker, Snowflake, and SQL.

Required Skills

• 6+ years of experience in data science or machine learning roles.
• Expertise in Python, specifically utilizing Pandas and NumPy for data manipulation.
• Proficiency in ML frameworks including TensorFlow, PyTorch, and Scikit-learn.
• Hands-on experience with deep learning architectures such as Neural Networks, RNNs, and Transformers.
• Experience with supervised learning methods like Logistic Regression, Decision Trees, and XGBoost.
• Practical knowledge of time-series forecasting using ARIMA, SARIMA, or Prophet.
• Ability to use AWS SageMaker and Snowflake for model deployment and data management.
• Solid SQL skills for data extraction and transformation.
• Familiarity with graph databases such as Neo4j or Memgraph.

Preferred Skills

• Experience applying reinforcement learning to financial risk models.
• Knowledge of graph-based models for fraud detection.

Key Skills

Education

Any Graduate