Quantitative Researcher

Sryantra Capital; New York (NY); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

PnL share

Proprietary datasets

Collaborative culture

Health/dental/vision coverage

Job summary

Sryantra Capital in New York seeks a quantitative researcher to design and backtest high-frequency trading signals using rigorous statistics and machine learning on massive datasets.

You will mine large-scale market data for predictive features and structural inefficiencies, research market microstructure, and collaborate with traders and developers to ship production-ready strategies while continuously adapting to changing markets.

Qualifications

• PhD or Master’s in a quantitative field (Mathematics, Physics, Computer Science, Statistics, or related).
• Strong programming skills in C++, Python, or Rust.
• Deep grounding in probability, statistics, and time-series analysis.
• Experience applying ML frameworks (PyTorch, TensorFlow, scikit-learn) to financial data.
• Curiosity, creativity, and a rigorous scientific approach.
• Fluency in English is required.

Responsibilities

• Design and backtest high-frequency trading signals using statistical and machine-learning methods.
• Mine large-scale market data for predictive features and structural inefficiencies.
• Research market microstructure and exchange dynamics with rigour.
• Improve portfolio construction and execution to reduce slippage and lift returns.
• Partner with traders and developers to ship production-ready strategies.
• Continuously monitor and adapt live strategies as markets change.

Skills

C++

Python

Rust

Time-series analysis

Statistics

Machine Learning

English fluency

Education

PhD or Master’s in a quantitative field

Tools

PyTorch

TensorFlow

scikit-learn

Job description

You'll work with massive datasets to build the predictive models behind our automated trading systems, applying rigorous statistics and machine learning to decode microstructure and anticipate price dynamics.

What You'll Do

• Design and backtest high-frequency trading signals using statistical and machine-learning methods.
• Mine large-scale market data for predictive features and structural inefficiencies.
• Research market microstructure and exchange dynamics with rigour.
• Improve portfolio construction and execution to reduce slippage and lift returns.
• Partner with traders and developers to ship production-ready strategies.
• Continuously monitor and adapt live strategies as markets change.

What You Bring

• A PhD or Master's in a quantitative field (Mathematics, Physics, Computer Science, Statistics, or related).
• Strong programming skills in C++, Python, or Rust.
• Deep grounding in probability, statistics, and time-series analysis.
• Experience applying ML frameworks (PyTorch, TensorFlow, scikit-learn) to financial data.
• Curiosity, creativity, and a rigorous scientific approach.
• Fluency in English is required.

What You'll Get

• A highly competitive package, including a share of the PnL you generate.
• Access to proprietary datasets and world-class compute.
• A collaborative research culture built on innovation and intellectual honesty.
• Full health, dental, and vision coverage.
• The chance to tackle some of the hardest problems in modern finance.