Quantitative Researcher

About the Role

Dolat Capital is building one of India's most advanced quantitative investment platforms, combining institutional-grade research, computational infrastructure, and systematic portfolio management to develop data-driven investment strategies across capital markets.

Our research philosophy is rooted in scientific validation, robust statistical testing, and disciplined execution. We believe investment decisions should emerge from evidence, not emotion.

We are looking for intellectually curious individuals who enjoy solving complex problems using mathematics, statistics, programming, and financial market data.

Role Overview

As a Quantitative Researcher, you will be responsible for developing, testing, and improving systematic investment strategies. You will work with large financial datasets, build quantitative models, validate research hypotheses, and contribute directly to live investment strategies.

This role offers exposure to institutional-scale quantitative research, high-performance computing infrastructure and access to large scale market datasets. You would be responsible for contributing to live strategies. Collaborate with experienced professionals across quantitative research, portfolio management, and technology.

Key Responsibilities

• Research and develop quantitative investment signals across multiple asset classes.
• Design and evaluate factor models, statistical models, and predictive algorithms.
• Perform rigorous backtesting, walk-forward validation, and robustness testing.
• Improve portfolio construction, risk management, and optimisation frameworks.
• Evaluate market behaviour using statistical and machine learning techniques where appropriate.
• Work closely with portfolio managers, technology teams, and data engineers to implement research into production.
• Continuously monitor live strategy performance and recommend model enhancements.

Desired Qualifications

Bachelor's, Master's, or PhD in: Mathematics, Statistics, Computer Science, Engineering, Finance.

Skills & Experience

• Strong understanding of probability, statistics, and linear algebra.
• Excellent programming skills in Python.
• Experience with SQL and financial data manipulation.
• Knowledge of machine learning is desirable.
• Understanding of portfolio theory, factor investing, and financial markets is preferred.
• Experience with backtesting frameworks is an advantage.
• Strong analytical and problem-solving ability.
• Ability to communicate research findings clearly.

Preferred Technical Skills

Python, Pandas, NumPy, SciPy, SQL, Git, Jupyter

Interested candidates

Interested candidates may apply with their resume, academic transcripts, and any relevant research papers, GitHub projects, Kaggle profiles, or quantitative research work.