Quantitative Researcher

Company Description

Crest Ventures Limited, headquartered in Mumbai, India, is a Non-Banking Finance Company registered with The Reserve Bank of India (RBI) and listed on both the National Stock Exchange of India (NSE) and Bombay Stock Exchange (BSE). Operating under three verticals—Real Estate, Financial Services, and Investments—the company has a 30-year legacy of building successful businesses in the Financial Services sector. Crest Ventures has a strong track record in creating high-quality residential and commercial real estate assets across major Indian cities, and actively invests in companies with high growth potential in both private and public markets. Our Trading and Research vertical is focused on building high-conviction systematic strategies across a range of asset classes. We blend quantitative research, data science and, and engineering discipline to create robust, adaptive strategies that perform across diverse market conditions. Join us on this exciting journey of growth and success!

About the Role

We are looking for a Quantitative Analyst / Researcher to join our strategy research team and contribute to the next generation of alpha-driven models across diverse systematic trading strategies and asset classes. This role offers the opportunity to lead high-impact research initiatives while working closely with experienced traders and technologists.

Responsibilities

Conduct research on macro and micro market regimes to guide strategy behavior and adaptability.

Develop and refine signal strength models that account for probability, context, and risk-reward dynamics.

Design and maintain a modular backtesting framework with realistic mark-to-market tracking and scenario testing.

Build and test systematic trading strategies across various timeframes, asset classes, and volatility conditions.

Leverage data science techniques to extract insights from large-scale historical and intraday market data.

Engineer and monitor features and indicators from technical, statistical, and derivatives-based inputs.

Use Volatility, Greeks, and structural market behavior to inform strategy design and optimization.

Contribute to the development of a comprehensive risk management framework including position sizing, exposure control, and drawdown rules.

Collaborate closely with trading and technology teams to transition research into live, capital-deployed strategies.

Continuously evaluate live performance and adapt strategies to evolving market conditions.

Qualifications & Skills

Bachelor’s, Master’s, or PhD in a quantitative discipline (e.g., Mathematics, Statistics, Engineering, Computer Science, Physics, or Quantitative Finance).

Solid understanding of financial markets, especially around price behavior, volatility, and market structure.

Familiarity with financial derivatives (futures, options) and their role in strategy construction and risk.

Strong background in data analysis, including statistical modeling, time-series analysis, and signal evaluation.

Proficient in Python and core data libraries (e.g., Pandas, NumPy, Scipy, Sckit-Learn); experience

with backtesting, data processing, or custom research tools is a plus.

Experience working with large-scale historical or intraday data, including data cleaning, transformation, and feature extraction.

Ability to design, test, and iterate on systematic trading ideas with a research-first

mindset.

Strong grasp of risk and performance metrics (Sharpe, drawdowns, exposure, etc.) and

how they translate to live deployment.

Comfortable working independently in a fast-paced environment while collaborating with

researchers, traders, and developers.

Bonus: Exposure to volatility modeling, options Greeks, machine learning, or custom backtesting frameworks.

Why Join Us

Be part of a firm that’s building cutting-edge, research-driven trading strategies across global markets.

Join a tight-knit, high-performing team where your ideas shape real outcomes and strategy direction.

Accelerate your learning through hands-on exposure to live trading, deep research, and cross-disciplinary collaboration.

Grow in an environment that values curiosity, experimentation, and long-term thinking over noise and shortcuts.