Quantitative Research & Development Intern

Who We Are Looking For
We are looking for a Quantitative Research & Development Intern to work with GreyOak’s quantitative research team on market data analysis, model development, backtesting and quantitative research.
The role is suited for students with a strong foundation in mathematics, probability, statistics, programming and data analysis, along with a genuine interest in financial markets and quantitative research.
You will work with historical and live financial market datasets, explore market behaviour, test quantitative hypotheses and contribute to the development and validation of proprietary quantitative models.
Internship Details
We are looking for candidates who can commit to a fixed internship period, either as a summer internship or semester-long internship.
Students in their third year, fourth year or final semester are preferred.
The internship is paid and includes a monthly stipend.
Candidates demonstrating strong performance and consistently meeting research and development expectations may be considered for a full-time role / PPO after completion of the internship.
Internship certificates will be issued upon successful completion of the agreed internship period.
We are looking for highly motivated candidates with strong academic and technical backgrounds.
Strong academic performance is preferred; a CGPA of 8.0+ / equivalent will be considered favourably.
What You Will Be Responsible For
As a Quantitative Research & Development Intern, you will:
Analyse historical and real-time financial market data.
Research statistical relationships, market patterns and quantitative behaviour across different instruments and timeframes.
Develop, test and validate quantitative models using Python.
Build systematic research pipelines for testing quantitative hypotheses.
Perform statistical analysis and feature engineering on financial datasets.
Conduct time-series analysis and identify meaningful market structures and relationships.
Backtest quantitative models across historical datasets.
Evaluate model performance using appropriate statistical and quantitative metrics.
Analyse model consistency across different market regimes and time periods.
Research probability-based approaches for measuring market behaviour and model confidence.
Work with large OHLCV and other structured financial datasets.
Develop data-processing and research utilities to improve the quantitative research workflow.
Identify model weaknesses, edge cases and potential overfitting.
Document research methodology, assumptions, results and conclusions.
Work closely with quantitative researchers and developers to convert research ideas into scalable models.
Contribute to improving existing quantitative models through testing, validation and experimentation.
What We Value
These skills will help you succeed in this role:
Strong understanding of probability and statistics.
Strong mathematical and analytical ability.
Good Python programming skills.
Experience working with NumPy, Pandas and similar data-analysis libraries.
Understanding of statistical modelling and hypothesis testing.
Knowledge of time-series analysis.
Familiarity with machine learning concepts and techniques.
Ability to analyse large datasets and identify meaningful patterns.
Understanding of model validation and backtesting methodologies.
Strong problem-solving ability and attention to detail.
Ability to independently investigate a research problem and present structured conclusions.
Preferred Technical Experience
Python for quantitative research and data analysis.
NumPy, Pandas, SciPy and Scikit-learn.
Time-series modelling and analysis.
Machine learning and statistical learning.
Feature engineering and pattern recognition.
Experience working with financial or market datasets.
Backtesting frameworks or experience building backtesting systems.
SQL and database fundamentals.
Git and version control.
Linux environment.
C++ or R knowledge is an advantage but not mandatory.
Knowledge of optimization techniques is an advantage.
Familiarity with quantitative finance concepts is preferred.
Education & Preferred Qualifications
Students pursuing B.Tech, B.E., M.Tech, M.Sc. or equivalent degrees in areas such as:
Mathematics and Computing
Computer Science
Mathematics
Statistics
Data Science
Artificial Intelligence / Machine Learning
Electrical Engineering
Electronics and Communication Engineering
Computational Finance
Financial Engineering
Candidates from other disciplines with a demonstrably strong background in mathematics, statistics and programming are also encouraged to apply.
Prior trading experience is not required. We care more about your ability to think quantitatively, work with data and rigorously test ideas.
About GreyOak
GreyOak Capital Intelligence is a fintech company building quantitative market intelligence products designed to make complex market data easier to interpret and use.
Our quantitative research team works with historical and real-time financial market data to develop proprietary models across market behaviour, price structures, expected ranges, opportunity discovery and quantitative intelligence.
The internship provides an opportunity to work on real quantitative research problems and contribute directly to models that can ultimately become part of production financial intelligence products.

Work arrangement
No