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Quantitative Analyst

Block MB
City of London
1 day ago
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Our client, are a global proprietary trading firm that combines sophisticated technology with deep quantitative research to deploy capital across global financial markets.


Role Overview

As a Quantitative Analyst, you will be at the heart of the trading and research efforts, designing and implementing statistical and mathematical models to identify and capitalise on market inefficiencies. You will work closely with traders, developers, and other quants in a fast-paced, intellectually demanding environment.


Key Responsibilities

  • Research, develop, and optimise trading models using large and complex datasets
  • Design and implement statistical, mathematical, and machine learning algorithms for signal generation and risk modeling
  • Perform backtesting and performance evaluation of strategies across various asset classes (equities, futures, options, FX, crypto, etc.)
  • Collaborate with technologists and traders to deploy models into production and monitor live trading systems
  • Contribute to portfolio construction, alpha combination, and execution optimization


Required Qualifications

  • PhD in Mathematics, Physics, Statistics, Computer Science, Engineering, or related quantitative field
  • Strong foundation in probability, statistics, stochastic processes, optimization, and/or numerical methods
  • Proficient programming skills in Python, C++, R, or MATLAB (Python preferred)
  • Demonstrated experience working with large datasets, data cleaning, and analysis
  • Familiarity with financial markets and instruments is a plus but not mandatory
  • Exceptional problem-solving skills and attention to detail
  • Ability to communicate complex ideas clearly and effectively


Desirable Experience (not required but a plus)

  • Experience in quantitative finance, algorithmic trading, or financial data analysis
  • Knowledge of machine learning frameworks and tools (e.g., scikit-learn, TensorFlow, PyTorch)
  • Exposure to high-frequency data and time-series analysis
  • Publications in peer-reviewed journals or participation in academic competitions (e.g., Kaggle, Olympiads)

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