The Investment Risk Data and Analytics team plays a pivotal role in developing data and analytical solutions. The team is part of Investment Risk Management Group and supports multiple investment risk managers (across Equity, Fixed Income and Alternatives) by developing data science solutions and machine learning techniques. If you enjoy working with complex data, developing advanced analytics and enjoy working in fast-paced environment, this role could be a great fit for you.

This role plays a crucial role for designing, developing, and implementing AI models and Machine Learning techniques to enhance investment risk management. The position involves working closely with investment professionals and risk managers to develop cutting-edge AI / ML models for effective risk assessment and decision-making processes.

• Data Preparation - Collects data from disparate systems, analyzes it, and delivers the data as intelligence that is actionable. Analyzes and interprets the results of research experiments through statistical models. Solves analytical problems utilizing large structured, semi-structured and un-structured data in a distributed processing environment.
• AI / ML Model Development - Builds and maintains predictive models to assess and quantify risks associated with various investment strategies and asset classes. Implements algorithms for tasks such as natural language processing to enhance investment risk oversight. Optimizes algorithms for speed, scalability, and efficiency, considering both training and inference phases.
• Collaboration and Communication - Works closely with cross-functional teams to understand business requirements and translate them into technical solutions. Communicates complex AI concepts and solutions to non-technical stakeholders.
• Bachelor’s / Master’s degree in Computer Science, Finance or AI / ML field
• Strong knowledge of financial markets, risk management, portfolio management or investment research
• 6+ years of proven experience in Data Science, predictive modeling using Statistics and Machine Learning
• Proficiency in programming languages such as Python, TensorFlow, PyTorch, or similar
• Knowledge of cloud platforms (e.g., AWS, Azure and GCP)
• Excellent problem-solving and analytical skills
• Strong communication and collaboration skills
• Strong Python and database skills
• Experience developing and implementing machine learning pipelines for generative AI and ML Models with a strong understanding of deep learning techniques such as GPT, VAE, and GAN