Description

Apply advanced statistical modeling and machine learning to financial market data, focusing on Fixed Income assets and investment banking.

Design and deploy statistical models and time series analysis for financial markets. Collaborate with data engineers to source, analyze, and engineer features for large-scale datasets. Develop, test, and deploy machine learning models within production environments. Build and manage ML and data pipelines to support application deployment via CI/CD. Present complex technical findings to technical peers and non-technical leadership.

Responsibilities

• Design and deploy statistical models and time series analysis for financial markets.
• Collaborate with data engineers to source, analyze, and engineer features for large-scale datasets.
• Develop, test, and deploy machine learning models within production environments.
• Build and manage ML and data pipelines to support application deployment via CI/CD.
• Present complex technical findings to technical peers and non-technical leadership.

Required Skills

• 14+ years of experience in data science, with at least 6 years in Financial, Investment, Asset Management, or Fixed Income sectors.
• Advanced proficiency in Python and/or R.
• Deep expertise in Machine Learning (Supervised, Unsupervised, Recommendation engines, Optimization) and Deep Learning (RNN, CNN, LSTM, Autoencoders, GANs).
• Hands-on experience with PySpark, Keras, and TensorFlow.
• Strong knowledge of Natural Language Processing and Generative AI/Language Models.
• Proficiency in SQL and at least one NoSQL database.
• Experience working with cloud platforms such as AWS, Azure, or IBM.
• Experience with CI/CD and ML pipeline deployment.
• Strong mathematical foundation in Machine Learning and statistical modeling.

Preferred Skills

• Experience in Asset Allocation, Research, or Quantitative Engineering.
• Background working in front-office roles within asset management firms.

Key Skills

Education

Bachelor's degree