Quantitative Researcher

Quantitative Research Intern (Part-Time, Hybrid Mumbai)

Company: GreyOak Capital Intelligence

Role: Quantitative Research Intern (Part-Time)

Location: Mumbai Fort (Hybrid)

Work Mode: 1 day/week in-office (Mumbai) + 710 hours/week remote

Duration: 3 months (extendable based on performance & mutual fit)

Start: Immediate / As mutually agreed

Role Overview

You will work closely with the Founder on quantitative research and product development for equity-focused research systems. This is a hands-on research role focused on experimentation, validation, and improving research qualityrather than slide decks, reporting, or passive analysis.

You will collaborate with in-house quantitative and backend engineering team members on research brainstorming, model design, and system-level thinking. The role involves cross-functional discussion between quant research and backend implementation, ensuring research ideas are practical, scalable, and production-aware.

Key Responsibilities

• Research and test quantitative ideas for Indian equities, including signals, filters, ranking logic, and regime/risk checks.
• Build and validate backtests with strong time-series discipline (no look-ahead bias, realistic assumptions).
• Perform feature engineering and support applied machine learning experiments (classification and regression).
• Conduct robustness checks such as out-of-sample testing, ablation studies, and failure-mode analysis.
• Improve research pipelines for clarity, speed, and reliability using clean, modular Python code.
• Maintain clear and structured research documentation: hypothesis method results conclusion.

Must-Have Skills

• Strong Python skills (pandas, numpy, scipy) with the ability to write clean, testable research code.
• Solid grounding in statistics, probability, and time-series analysis.
• Understanding of backtesting methodology, evaluation metrics, and overfitting control.
• Basic applied ML knowledge, including feature design, validation, and model selection.
• Ability to work independently and communicate research findings clearly.

Good-to-Have (Plus)

• Familiarity with Indian equity markets and real-world trading constraints.
• Experience with XGBoost, LightGBM, CatBoost, or factor-based research.
• Exposure to portfolio risk metrics, SQL, Git, or basic data engineering workflows.

Ideal Candidate Profile

Someone with a strong quantitative or financial-engineering mindset, comfortable with research ambiguity, experimentation, and iterative model improvementsimilar to profiles in quantitative research, financial engineering, or applied machine learning.

Confidentiality

This role involves proprietary research. NDA and strict confidentiality are mandatory.

How to Apply

Please email [Confidential Information]

with:

• Your resume (and GitHub or research code links, if available)
• 23 bullets describing your best quantitative work (strategy, model, backtest, or risk project)