Quant Researcher - Machine Learning

Findoc Investmart Private; Gurgaon; Hybrid; INR 1,200,000 - 2,000,000; Full time

Job summary

A leading financial technology firm is looking for a Quantitative ML Researcher to join its HFT trading team in Gurgaon, India. This hybrid role involves designing and optimizing statistical and machine learning models along with developing high-performance trading tools in C++, Python, or Rust. The ideal candidate will work closely with traders and researchers to analyze high-frequency data and improve algorithms for execution efficiency. Strong skills in statistical modeling and data analysis are essential for this crucial position.

Qualifications

• Strong background in statistical and machine learning-based modeling.
• Experience in developing high-performance, low-latency code.
• Proficient in analyzing high-frequency trading data.

Responsibilities

• Design and optimize statistical and ML-based trading models.
• Collaborate with teams to backtest and deploy strategies.
• Build tools for feature engineering and model evaluation.
• Improve execution algorithms for better trading efficiency.

Skills

Statistical modeling

Machine learning

High-performance coding

Data analysis

Feature engineering

Tools

C++

Python

Rust

Job description

Findoc Investmart Private Ltd | Full time

We are seeking a highly skilled Quantitative ML Researcher to join our HFT trading team. You’ll be working at the intersection of quantitative research, machine learning, and high-performance software engineering, helping to develop, implement, and optimize trading strategies deployed in global financial markets.

This is a hybrid research-engineering role where you'll collaborate with traders, researchers, and infrastructure engineers to create cutting-edge tools and models that drive our trading decisions.

Requirements

• Design, implement, and optimize statistical and ML-based trading models.
• Develop high-performance, low-latency code in C++, Python, or Rust.
• Analyze large-scale, high-frequency data to identify predictive signals (alpha).
• Collaborate with research and trading teams to backtest and deploy strategies in live environments.
• Build tools for feature engineering, data normalization, and model evaluation.
• Improve execution algorithms for minimizing slippage, market impact, and latency.
• Contribute to building robust infrastructure for automated model training and deployment.