Quantitative Analyst (AI) II

Key Responsibilities

• Develop, validate, and maintain quantitative models for pricing, forecasting, portfolio optimization, and risk analysis.
• Apply machine learning and artificial intelligence techniques to improve predictive modeling and decision-making.
• Analyze structured and unstructured datasets to identify trends, patterns, and investment opportunities.
• Design statistical models for time-series forecasting, anomaly detection, classification, and regression.
• Build and evaluate predictive models using historical and real-time financial data.
• Collaborate with engineering teams to deploy quantitative models into production environments.
• Perform backtesting, model validation, stress testing, and performance evaluation.
• Develop data pipelines and automate quantitative analysis workflows.
• Monitor model performance and recommend improvements based on changing market conditions.
• Document methodologies, assumptions, and model validation results.
• Ensure compliance with model governance, regulatory standards, and risk management policies.
• Present analytical findings and recommendations to technical and business stakeholders.

Required Qualifications

• Bachelor's or Master's degree in Quantitative Finance, Mathematics, Statistics, Computer Science, Data Science, Economics, Engineering, or a related quantitative discipline.
• 35+ years of experience in quantitative analysis, financial modeling, machine learning, or data science.
• Strong programming skills in Python.
• Solid understanding of statistics, probability, linear algebra, optimization, and numerical methods.
• Experience with machine learning libraries such as Scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Experience working with SQL and large datasets.
• Knowledge of financial instruments, market data, and quantitative finance concepts.
• Experience with data visualization and reporting tools.
• Strong analytical and problem-solving skills.

Preferred Qualifications

• Experience in algorithmic trading, portfolio optimization, or quantitative investment strategies.
• Knowledge of deep learning, reinforcement learning, or Generative AI for financial applications.
• Experience with time-series forecasting models such as ARIMA, Prophet, or LSTM networks.
• Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Experience deploying machine learning models using MLOps practices.
• Understanding of financial risk frameworks and regulatory requirements.
• Professional certifications such as CFA, FRM, or CQF are an advantage.