Quantitative Finance

4 weeks ago


Bengaluru, Karnataka, 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
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