Quantitative Researcher

Bitqcode quantitative capital; Dubai; On-site; AED 350,000 - 750,000; Full time

Job summary

Bitqcode Quantitative Capital is seeking a quantitative researcher to design and implement trading strategies across global markets. You will leverage statistical and machine learning models to research signals, backtest strategies on large tick-data, and develop alpha-generating ideas for execution across multiple exchanges.

The role requires strong programming skills (Python/C++/Rust), knowledge of market microstructure, and experience with high-frequency data and backtesting frameworks.

Qualifications

• Experience with global financial markets and exchange mechanics.
• Strong coding skills in Python, C++, or Rust with numerical computing and data wrangling.
• Familiarity with machine learning techniques rooted in statistics and model validation.
• Experience handling high-frequency data, order book reconstruction, and backtesting.

Responsibilities

• Research and implement quantitative trading strategies across global markets.
• Conduct alpha research, signal generation, and strategy backtesting on tick data.
• Develop statistical arbitrage techniques across asset classes and exchanges.
• Model market microstructure phenomena such as latency and order book dynamics.
• Collaborate with engineering to deploy low-latency strategies in production.
• Monitor and improve model performance using real-time and historical data.

Skills

Python

C++

Rust

Machine Learning

Time Series

Data Wrangling

Low Latency

API Interaction

Education

Bachelor's degree in quantitative field

Master's degree in quantitative field

PhD in quantitative field

Tools

CUDA

NumPy/Pandas

Git

Job description

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

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

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.