Quantitative Researcher
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.
About the client:
Our client is a Gibraltar-based trading and investment management company. The company specializes in quantitative, market-neutral digital assets trading strategies. The team operates on both centralized and decentralized markets to bring uncorrelated, absolute returns performance to the clients.
The company uses proprietary technology and algorithms developed by a blend of crypto and traditional finance veterans. The team members have been in conventional finance since 2006 and in digital assets since 2018.
About the role:
We are looking for a Quantitative Researcher, who will join the team. You’ll partner with the investment team to develop and evaluate crypto‐focused quantitative strategies. You’ll analyze latency‐optimization logs, back-test models in live environments, and deliver data-driven feedback to refine the trading signals. This role is modeled on Quantitative Researcher framework, emphasizing rigorous research, statistical analysis, and rapid implementation of algorithms into code.
Requirements:
– 1–3 years experience in Computer Science, Machine Learning, or a similarly quantitative field.
– Deep proficiency in Python and Pandas for data manipulation and analysis.
– Strong foundation in statistics and mathematics (e.g., probability, time-series analysis, hypothesis testing).
– Prior exposure to data-driven research or ML model development.
– STEM background (e.g., CS, Math, Engineering) is a must.
- Knowledge of finance or digital-asset markets is appreciated, but not required.
- Familiarity with unconventional data sources, version control, and automated testing frameworks.
Responsibilities:
– Conduct quantitative research on crypto markets and evaluate strategy performance.
– Analyze large datasets and latency logs to identify bottlenecks and optimize trade execution.
– Conceptualize and continuously refine mathematical models; translate algorithms into production-ready Python/Pandas code.
– Back-test and implement trading signals in a live environment, monitoring real-time performance.
– Collaborate with traders, engineers, and data teams to integrate findings and improve strategy robustness.
The company offers:
– Base salary depending on experience.
– Discretionary year-end trading bonus.
– Opportunities for professional growth in an innovative trading environment.
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