Description

You will lead machine learning development for payments and risk management systems. This role is on-site.

Responsibilities

• Develop, train, and deploy supervised models (Logistic Regression, Decision Trees, XGBoost, LightGBM) for fraud prediction and merchant risk assessment.
• Implement deep learning architectures (Neural Networks, RNNs, Transformers) for fraud detection, sequence modeling, and processing unstructured data.
• Design graph-based solutions using Neo4j or Memgraph to identify complex fraud patterns.
• Construct time-series models (ARIMA, SARIMA, Prophet, ETS) for loss forecasting and scenario analysis.
• Manage the end-to-end model lifecycle using AWS SageMaker, Snowflake, and SQL.

Required Skills

• 6+ years of experience in machine learning and data science.
• Strong proficiency in Python with Pandas and NumPy.
• Expertise in TensorFlow, PyTorch, and Scikit-learn.
• Hands-on experience with AWS SageMaker and Snowflake.
• Solid SQL skills for data extraction and manipulation.
• Experience with graph databases (Neo4j, Memgraph) and centrality measures.
• Knowledge of time-series forecasting techniques.

Preferred Skills

• Experience implementing reinforcement learning.
• Background in merchant underwriting and creditworthiness modeling.

Key Skills

Education

Any Graduate