100% Remote

Quantitative Finance Professional with Data Science, Python

Contract

Position Overview

We are seeking a highly analytical finance professional to develop and implement data-driven investment and risk management strategies. This role focuses on systematic, model-based approaches to portfolio construction, derivatives analysis, and financial risk control. The ideal candidate combines strong quantitative skills with practical experience in financial markets.

Key Responsibilities

1. Systematic Strategy Development

• Design and implement rules-based investment strategies.
• Build algorithmic models for asset allocation, signal generation, and trade execution.
• Continuously improve models using statistical and machine learning techniques.

2. Derivatives & Structured Products

• Analyze and price options, futures, swaps, and other derivatives.
• Develop hedging strategies to manage market exposure.
• Monitor derivative positions and manage associated risks.

3. Risk Management & Analytics

• Measure and monitor market, credit, and liquidity risks.
• Calculate and interpret Value at Risk (VaR) and stress testing metrics.
• Develop risk dashboards and reporting frameworks.

4. Factor-Based Portfolio Management

• Research and implement factor models (e.g., value, momentum, volatility).
• Construct and optimize multi-factor portfolios.
• Evaluate factor performance across asset classes.

5. Back-Testing & Performance Analysis

• Conduct rigorous historical back-testing of trading strategies.
• Validate models and ensure robustness under different market conditions.
• Analyze alpha generation and benchmark-relative performance.

Required Qualifications

• Bachelor’s or Master’s degree in Finance, Mathematics, Statistics, Engineering, Physics, or related quantitative field.
• Strong programming skills (Python required; experience with SQL, R, or C++ is a plus).
• Solid understanding of financial markets, derivatives, and portfolio theory.
• Experience with data analysis, statistical modeling, and financial datasets.
• Knowledge of risk metrics (VaR, CVaR, stress testing frameworks).

Preferred Skills

• Experience with machine learning in financial applications.
• Familiarity with trading systems and market microstructure.
• Knowledge of optimization techniques and factor modeling.
• Strong problem-solving and critical thinking abilities.
• Clear communication skills to explain complex quantitative concepts.