Description

You will lead the development of machine learning solutions for financial transaction monitoring and market analysis.

Responsibilities

• Implement ML for Exception Classification (MLEC) within financial transactions.
• Develop Early Warning Signals for anomaly detection in time-series data.
• Build Model Surveillance frameworks for financial models.
• Apply unsupervised clustering techniques for market segmentation in securities lending.
• Develop advanced data quality control frameworks using TensorFlow-based validation.
• Configure storage and repositories for large-scale data ingestion.

Required Skills

• 12+ years of experience in quantitative development.
• 10+ years of experience in C++.
• 7+ years of experience in quantitative analysis and mathematical modeling.
• Expertise in supervised and unsupervised machine learning.
• Proficiency in Python, TensorFlow, SQL, and Jupyter Notebooks.
• Deep understanding of time-series modeling, anomaly detection, and risk analytics.
• Experience with big data processing and financial data pipelines.
• Any Graduate degree.

Preferred Skills

• Prior experience in investment banking, asset management, or trading desks.
• Strong foundation in quantitative finance and financial modeling.
• Hands-on experience with PyTorch and AWS/GCP AI services.

Key Skills

Education

Any Graduate