Job Title: Quantitative Analyst (AI) IIJob Summary We are seeking a Quantitative Analyst (AI) II to develop and implement AI-driven quantitative models that support financial analysis, risk management, forecasting, pricing, and investment strategies. The ideal candidate combines expertise in quantitative methods, statistics, machine learning, and programming to solve complex business and financial problems. This role partners with data scientists, software engineers, portfolio managers, risk analysts, and business stakeholders to build scalable, data-driven solutions.
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.
- 3-5 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.
Technical Skills - Python
- SQL
- Pandas
- NumPy
- SciPy
- Scikit-learn
- XGBoost
- TensorFlow
- PyTorch
- Jupyter Notebook
- Git
- Tableau / Power BI
- AWS / Azure / Google Cloud Platform
- MLflow (preferred)
- Docker (preferred)
Soft Skills - Strong analytical and quantitative reasoning
- Excellent communication and presentation skills
- Attention to detail and accuracy
- Ability to explain complex analytical concepts to non-technical stakeholders
- Collaboration and teamwork
- Continuous learning and innovation mindset
Nice to Have - Experience with alternative data sources and feature engineering for financial models
- Knowledge of natural language processing (NLP) for financial text analysis
- Experience with Large Language Models (LLMs) for financial research and document analysis
- Familiarity with distributed computing frameworks such as Spark or Ray
- Experience with Responsible AI and model governance practices
Key Performance Indicators (KPIs) - Predictive accuracy of quantitative models
- Model stability and performance over time
- Timely delivery of analytical solutions
- Improvement in forecasting and risk assessment accuracy
- Reduction in model errors and validation issues
- Adoption and business impact of AI-driven models
- Compliance with model governance and documentation standards
Location Hybrid / Remote / On-site (as applicable)
Employment Type Full-time
