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Quantitative Finance

1 month ago

india Research Commons Full time


Quantitative Finance (Intern) – Urgent Hiring


Location: Remote

Stipend: 5K INR per month

Duration: 3 Months


About Research Commons:


Research Commons is an early-stage startup founded by IIT and BITS Pilani alums dedicated to revolutionizing the academic research process. Our platform is designed for researchers at all stages of their careers-from first-time authors to seasoned experts. We provide innovative tools to help researchers publish, analyze, and collaborate seamlessly. Our mission is to empower aspiring researchers by offering a comprehensive ecosystem that accelerates scientific discovery.


Join us in building a one-stop shop for research tooling, a collaborative community, and career opportunities for academics.


Learn more about us here:


About the Role:

We are looking for a Quantitative Finance Intern to join our team and work on developing, backtesting, and optimizing quantitative trading strategies . This role offers a unique opportunity to gain hands-on experience in financial modeling, risk analysis, and market forecasting. You will collaborate with a skilled team to analyze large datasets, implement statistical models, and enhance automation for financial research.


Responsibilities:

  • Develop and backtest quantitative trading strategies using Python or R.
  • Analyze large financial datasets to extract key insights.
  • Work on portfolio optimization, risk assessment, and forecasting models .
  • Implement and refine statistical and machine learning models for market predictions.
  • Assist in automating data collection and visualization for financial research.


Required Skills:

  • Strong foundation in mathematics, statistics, and probability .
  • Proficiency in Python, R, or MATLAB for data analysis.
  • Understanding of financial markets, risk management, and trading strategies .
  • Familiarity with time-series analysis, econometrics, and quantitative finance .
  • Knowledge of Monte Carlo simulations, Black-Scholes model, VaR, and factor models is a plus.
  • Ability to work independently and handle large datasets efficiently.


Preferred Qualifications:

  • Pursuing a degree in Quantitative Finance, Financial Engineering, Mathematics, Statistics , or related fields.
  • Experience with Bloomberg Terminal, Quandl, Yahoo Finance API , or other market data sources.
  • Interest in machine learning and AI applications in finance .


Please reach out to @priyadarshishaket