Risk Program Senior Associate

What you'll do

• Identify and adapt machine learning algorithms for fraud detection.
• Engineer and select features, and develop and train models using large transaction datasets.
• Work with business teams to identify needs and guide use of machine learning solutions; share findings and expertise across the firm.

What they ask for

• Master’s degree in a related quantitative field.
• 2+ years of experience with data analysis in Python.
• Commercial model-design experience using at least 3 listed techniques: CNN/RNN, Reinforcement Learning, Random Forest/GBM, Transformer based architecture, and Graph technology.
• Strong interest in model behavior, why models work or fail, and practical model design.

Nice to have

• PhD in a quantitative field with publications in top journals, preferably in machine learning.
• Experience with cloud and distributed/parallel processing, graph-based machine learning for fraud or risk, or transformer models and frameworks such as PyTorch or TensorFlow.

Tools

Python, CNN/RNN, Reinforcement Learning, Random Forest/GBM, Transformer based architecture, Graph technology, Databricks, AWS

AI focus

Machine learning for fraud detection and risk assessment, including feature engineering and selection, model development and training, graph analytics, and transformer applications.