Quantitative Researcher

Unchain Data; Bengaluru; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Unchain Data is seeking a highly driven Quantitative Researcher with a strong foundation in statistics and market microstructure to join our crypto trading team. The role focuses on high-frequency trading, alpha discovery, and rigorous backtesting across crypto assets on both centralized and decentralized exchanges.

Ideal candidates have hands-on experience building and testing trading strategies, with expertise in ML techniques grounded in solid statistics, and a deep understanding of order

Qualifications

• Strong foundation in statistics, mathematics, and market microstructure.
• Experience with crypto markets and order book mechanics (CEX/DEX).
• Proficiency in Python/C++/Rust and numerical computing.
• Experience in backtesting, execution algorithms, and ML methods grounded in statistics.

Responsibilities

• Research and design quantitative trading strategies for crypto markets.
• Backtest strategies using large-scale historical tick/order book data.
• Develop alpha signals and arbitrage approaches across exchanges.
• Model market microstructure phenomena like latency arbitrage and order book dynamics.
• Collaborate with engineering to deploy low-latency strategies in production.
• Monitor model performance with real-time data and adjust accordingly.

Skills

Probability theory

Stochastic processes

Time series analysis

Optimization

Python

C++

Rust

Machine learning

Data wrangling

API interaction

Education

Bachelor's degree

Master's degree

PhD

Tools

NumPy

SciPy

Pandas

CUDA

Job description

About The Role

We are seeking a highly driven and analytical Quantitative Researcher with a strong foundation in mathematics, statistics, and market microstructure to join our crypto trading team. This role is ideal for candidates passionate about high-frequency trading (HFT), statistical arbitrage, and innovative alpha discovery in decentralized and centralized digital asset markets.

The ideal candidate should have hands‑on experience in developing and testing trading strategies, coupled with a deep understanding of order book dynamics, risk modeling, and ML techniques grounded in sound statistical reasoning, not just generic algorithmic applications.

Responsibilities

• Research, design, and implement quantitative trading strategies in crypto markets using statistical and machine learning models.
• Conduct alpha research, signal generation, and strategy backtesting using large‑scale historical tick/order book data.
• Develop and apply statistical arbitrage techniques across multiple crypto assets and exchanges (both CEXs and DEXs).
• Model market microstructure phenomena such as latency arbitrage, limit order book dynamics, and short‑term price impact.
• Perform rigorous data analysis and hypothesis testing to validate trading ideas and monitor live strategies.
• Collaborate with engineering teams to deploy strategies in production environments with low‑latency constraints.
• Continuously monitor and improve model performance using real‑time and historical data.
• Stay abreast of latest developments in DeFi, crypto trading infrastructure, and quantitative finance research.

Requirements

• Bachelor's, Master's, or PhD in Statistics, Mathematics, Physics, Computer Science, or a related quantitative field.
• Solid knowledge of probability theory, stochastic processes, time series analysis, and optimization.
• Proven experience with crypto markets, including knowledge of CEX/DEX mechanics, liquidity provision, and volatility regimes.
• Strong coding skills in Python, C++, or Rust, with experience in numerical computing, data wrangling, and API interaction.
• Familiarity with machine learning techniques rooted in statistical principles (Bayesian methods, Gaussian Processes, feature selection, model validation).
• Experience in handling high‑frequency data, order book reconstruction, and building execution algorithms.
• Ability to design robust backtesting frameworks and simulate strategy performance under varying market conditions.

Preferred Qualifications

• Prior experience in a quant fund, HFT firm, or crypto‑native trading desk.
• Familiarity with cloud computing, GPU acceleration, or high‑performance computing techniques.
• Exposure to alternative data, chain analytics, or DeFi protocols.
• Strong understanding of execution cost modeling, slippage, and latency optimization.