Назад
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14 дней назад

ML Lead (Quantitative Trading)

12 000$
Формат работы
remote (только Europe)
Тип работы
fulltime
Грейд
senior/lead
Английский
b2
Страна
Serbia
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
ML Lead (Quantitative Trading): Developing and improving ML-driven trading strategies, quantitative models, and research pipelines for forecasting and signal generation with an accent on time-series analysis, market data, and statistical modeling. Focus on validating trading hypotheses, optimizing algorithm speed, and bringing profitable strategies into production-ready state.

Location: Remote setup with flexible hours

Salary: Up to $12,000 net per month plus a 4–10% PnL share, negotiated individually

Company

hirify.global is a low-bureaucracy quantitative trading team with a main office in Serbia and a distributed team.

What you will do

  • Develop and improve algorithmic trading strategies.
  • Build machine learning and quantitative models for forecasting and signal generation.
  • Analyze time-series and real-time market data.
  • Generate, test, and validate hypotheses, signals, and models.
  • Optimize algorithm speed and efficiency and work with research and backtesting infrastructure.
  • Help establish new directions and potentially grow a research team.

Requirements

  • Senior or lead-level experience in ML-driven trading or quantitative research.
  • Proven experience developing profitable trading or machine learning-driven strategies.
  • Strong Python and machine learning skills, including NumPy, Numba, PyTorch or JAX, and scikit-learn.
  • Strong mathematical and statistical background with experience working with time-series and market data.
  • Experience building, testing, and validating trading hypotheses, signals, and models.
  • Understanding of trading principles, market structure, and market data.

Nice to have

  • Ability to read, analyze, and optimize C++ code.
  • Experience with production machine learning systems, HFT, arbitrage, market-making, or low-latency systems.
  • Competitive programming or a strong academic background, including ICPC, Kaggle, or Yandex SHAD.
  • Publications at top-tier machine learning conferences or strong results in ML, data science, or quantitative competitions.
  • Experience developing complex machine learning and deep learning models.

Culture & Benefits

  • Small, low-bureaucracy team with fast decision-making.
  • Rapid hypothesis testing and a research-focused working environment.
  • Distributed team with a main office in Serbia.
  • Remote work and flexible hours.
  • Performance-based PnL share of 4–10%.

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