About the role

You will research, design, and prototype quantitative models for pricing, risk management, and market making while building robust backtesting frameworks. Additionally, you will collaborate with engineering teams to implement these models and conduct post-incident analysis to optimize performance.

What they look for

Python NumPy Pandas SciPy Scikit-learn Statsmodels SQL Probability theory Statistics Time-series analysis Financial mathematics Machine learning Market microstructure Risk metrics Backtesting Algorithmic trading

Requirements

The role requires 3+ years of experience as a Data Scientist or Quantitative Researcher with a strong background in probability, statistics, and financial mathematics. Proficiency in Python and SQL is essential, along with a deep understanding of market microstructure and risk management mechanisms.

Benefits

20 paid vacation days per year 10 paid sick leave days per year Public holidays Medical insurance Remote work opportunity Professional education budget Language learning budget Wellness budget

Full description

We are looking for a Data Scientist to become the data-driven backbone of our Trading Core and Risk Tech squads. In a high-frequency trading environment processing billions in volume, success is defined by precision.

In this role, you will research, model, and validate the core data-driven models that drive our pricing engines, automated market-making algorithms, risk management frameworks (A/B/C-book optimization), and liquidation mechanics. You will sit at the intersection of statistics, data science, and high-performance software engineering, working directly with Core Product Managers and engineering teams to turn complex financial data into proprietary algorithmic advantages.

Responsibilities

Research, design, and prototype behavioral, risk, and toxicity-scoring models for client and partner-flow segmentation

Build backtesting and monitoring frameworks to validate models, signals, and hypotheses across data of varying granularity

Detect early risk signals, anomalies, and regime shifts in market and client behavior, including probability of critical capital loss

Develop explainable risk signals and labels for the R&D team, and long-term client value/risk models with forecasting

Conduct research and hypothesis-testing on client economics, flow quality, and model performance

Requirements

3+ years of experience as a Data Scientist / Quantitative Researcher

Exceptional knowledge of probability theory, statistics, time-series analysis, and financial mathematics

Advanced proficiency in Python (NumPy, Pandas, SciPy, Scikit-learn, Statsmodels) for data analysis, modeling, and backtesting

Solid experience with Machine Learning

Deep understanding of market microstructure, order book dynamics, risk metrics (VaR, Expected Shortfall), and margin/liquidation mechanisms

SQL skills and experience working with large-scale historical market data (tick data, order logs)

Strong logical thinking, initiative, and well-developed communication skills

Will be a plus

Experience in CFD, Crypto CEX, Prop Trading Firm, or Hedge Fund

Degree (MSc or PhD preferred) in a highly quantitative field: Mathematics, Physics, Statistics, Quantitative Finance, or Computer Science

Understanding of Asset pricing models (e.g., Black-Scholes, local volatility models, Greeks management)

Knowledge of the MetaTrader platforms (MT4/MT5)

Experience with AI (Claude.io, Copilot, Codex)

We offer

20 paid vacation days per year

10 paid sick leave days per year

Public holidays as per the company's approved Public holiday list

Medical insurance

Opportunity to work remotely

Professional education budget

Language learning budget

Wellness budget (gym membership, sports gear and related expenses)