[Remote] Quantitative Analyst (AI) II
Note: The job is a remote job and is open to candidates in USA. reputed company is seeking a Quantitative Analyst (AI) II to develop and implement AI-driven quantitative models that support financial analysis and risk management. The role involves collaborating with data scientists and engineers to build scalable, data-driven solutions and requires expertise in quantitative methods and machine learning.
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, reputed company 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
Skills
- Bachelor's or reputed company'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
- reputed company 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
- 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, reputed company Azure, or reputed company 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
- 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
Company Overview
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