Quantitative Researcher

Bitqcode Quantitative Capital; Bengaluru; On-site; INR 2,400,000 - 4,200,000; Full time

Job summary

Bitqcode Quantitative Capital in Bengaluru seeks a highly driven Quantitative Researcher with a strong foundation in mathematics and market microstructure to join its systematic trading team. You will develop, backtest, and productionize strategies across global markets including equities, futures and FX, emphasizing low-latency execution.

The role requires hands-on experience with high-frequency data, backtesting frameworks, and ML techniques grounded in statistics.

Qualifications

• Bachelor's, Master's, or PhD in Statistics, Mathematics, Physics, Computer Science, or a related quantitative field.
• Strong knowledge of probability, stochastic processes, time series analysis, and optimization.
• Experience with global financial markets, including exchange mechanics, liquidity, and volatility regimes.
• Strong coding skills in Python, C++, or Rust, with numerical computing and data wrangling experience.
• Familiarity with ML techniques rooted in statistics (Bayesian methods, Gaussian Processes, feature selection, model validation).
• Experience 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.

Responsibilities

• Research, design, and implement quantitative trading strategies across global markets.
• Backtest using large-scale historical tick/order book data.
• Develop statistical arbitrage techniques across asset classes and exchanges.
• Model market microstructure phenomena like latency arbitrage and order book dynamics.
• Perform rigorous data analysis and hypothesis testing to validate ideas.
• Collaborate with engineering to deploy strategies in production with low latency.
• Monitor and improve model performance using real-time data.
• Stay updated on trading infrastructure and quantitative finance research.

Skills

Quantitative research

Python

C++

Rust

Statistics

Time series

Machine learning

Education

Bachelor's/Master's/PhD in Statistics, Mathematics, Physics, or CS

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 systematic trading team. This role is ideal for candidates passionate about high-frequency trading (HFT), statistical arbitrage, and innovative alpha discovery across global financial markets — equities, futures, options, FX, and commodities.

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.

Key Responsibilities

• Research, design, and implement quantitative trading strategies across global 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 asset classes, instruments, and exchanges.
• 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 trading infrastructure, execution technology, and quantitative finance research.

Required Qualifications

• 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 global financial markets, including knowledge of exchange 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 systematic trading desk.
• Familiarity with cloud computing, GPU acceleration, or high-performance computing techniques.
• Exposure to alternative data, non-traditional datasets, and novel signal sources.
• Strong understanding of execution cost modeling, slippage, and latency optimization.