Quantitative Researcher

Albert Bow Limited; Gurugram District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Albert Bow Limited in Gurugram, India seeks a Quantitative Researcher to design and deploy machine learning models that forecast movements in liquid financial assets. You will work with large datasets to identify alpha signals and contribute to diversified, market‑neutral strategies.

The role emphasizes developing and backtesting strategies, applying cutting‑edge ML research, and collaborating with a highly technical team to scale models across markets.

Qualifications

• BS, MS, or PhD in a STEM‑related discipline.
• 3+ years of experience in a systematic trading or quantitative research environment.
• Strong interest and practical experience in machine learning.
• Strong programming skills, particularly in Python.
• Excellent analytical and problem‑solving abilities.
• A research‑oriented mindset and curiosity about financial markets.
• Ability to work independently while collaborating within a highly technical team.

Responsibilities

• Design, implement, and optimise machine learning models for predicting movements in liquid financial assets.
• Work with large and diverse financial datasets to identify actionable alpha signals.
• Develop and refine systematic trading strategies using statistical and ML techniques.
• Research and apply state‑of‑the‑art academic developments in quantitative finance and ML.
• Integrate new datasets, features, and modelling techniques to improve strategy performance and scalability.
• Conduct experiments, backtest hypotheses, and rigorously evaluate strategies through simulations and data analysis.
• Collaborate with quantitative researchers and strategy teams to turn research ideas into robust systematic strategies.

Skills

Machine Learning

Analytical thinking

Problem solving

Research mindset

Independent working

Education

BS/MS/PhD in STEM

Tools

Python

Job description

Quantitative Researcher | Gurugram, India

We’re working with a leading systematic investment firm that uses large-scale statistical modelling, machine learning, and alternative datasets to develop systematic trading strategies across global financial markets.

The research team works with extensive datasets to engineer features, identify predictive signals, and combine them into diversified, market-neutral portfolios.

What You’ll Do:

• Design, implement, and optimise machine learning models for predicting movements in liquid financial assets.
• Work with large and diverse financial datasets to identify actionable alpha signals.
• Develop and refine systematic trading strategies using statistical and machine learning techniques.
• Research and apply state-of-the‑art academic developments in quantitative finance and machine learning.
• Integrate new datasets, features, and modelling techniques to improve strategy performance and scalability.
• Conduct experiments, backtest hypotheses, and rigorously evaluate strategies through simulations and data analysis.
• Collaborate with quantitative researchers and strategy teams to turn research ideas into robust systematic strategies.

What We’re Looking For:

• BS, MS, or PhD in a STEM‑related discipline.
• 3+ years of experience in a systematic trading or quantitative research environment.
• Strong interest and practical experience in machine learning.
• Strong programming skills, particularly in Python.
• Excellent analytical and problem‑solving abilities.
• A research‑oriented mindset and curiosity about financial markets.
• Ability to work independently while collaborating effectively within a highly technical team.

If you’re a quantitative researcher who enjoys working at the intersection of machine learning, data, and systematic trading, I’d be happy to connect.