Python Trading Research Analyst at Freedx

, , Kazakhstan -

Full Time

Start Date

Immediate

Expiry Date

17 Feb, 26

Salary

0.0

Posted On

19 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Market Data Analysis, Statistical Modelling, Machine Learning, Algorithm Analysis, Backtesting, Simulation, Pandas, Numpy, Scipy, Sklearn, Statsmodels, Statistics, Deep Learning, PostgreSQL, Crypto Markets

Industry

Financial Services

Description

We’re looking for a smart, curious, and analytical junior researcher to join our trading team. If you enjoy working with Python, exploring market data, and turning numbers into insights, this role will give you hands-on experience in real-world trading environments and market-making research. About Freedx Freedx is a newly established cryptocurrency exchange dedicated to delivering a seamless, secure, and innovative trading experience. Built by a team of seasoned professionals from the crypto, finance, and technology sectors, we’re united by a shared passion for shaping the future of digital finance. Our vision is to become the most trusted and reliable exchange, empowering users with the freedom to manage their digital assets confidently and efficiently. Security, transparency, and scalability are at the core of everything we do. We continuously innovate to meet the evolving needs of traders, offering cutting-edge tools and services that help our clients achieve their financial goals. Join us as we redefine the standards of digital asset trading and bring true freedom and reliability to the crypto world. What You Will Do 1. Market Data Analysis Analyze orderbook data: spreads, depth, liquidity layers. Study market microstructure (order flow, volatility patterns). Examine how markets behave across different volatility regimes. Research basis, funding, contango/backwardation, cross-exchange deviations. 2. Statistical Modelling Build basic statistical models to detect regime shifts and volatility clusters. Explore relationships between features and realized PnL. Develop simple metrics to evaluate market quality and liquidity. 3. Machine Learning Research Apply ML methods to orderbook features and short-horizon price prediction. Evaluate models for stability, latency impact, and overfitting risks. 4. Algorithm Analysis Test hypotheses and ideas from traders. Structure your findings into models that can feed into production strategies. 5. Backtesting & Simulation Build lightweight simulation environments for MM and directional strategies. Test strategy variations under different market assumptions. Model hedging effectiveness across exchanges/instruments. Run scenario-based stress tests (vol spikes, price dislocations, liquidity dry-ups). Strong proficiency in Python, especially pandas, numpy, scipy, sklearn, statsmodels. Understanding of machine learning techniques for time series or microstructure-style data. Solid foundation in statistics: distributions, variance, regressions, probability. Experience building models for prediction, classification, or clustering (projects, coursework, or work experience). Nice to Have Experience with deep learning for timeseries (PyTorch or TensorFlow). Knowledge of PostgreSQL/TimescaleDB for large dataset queries. Experience analyzing tick-level or Level 2 data from Binance/KuCoin/Bybit/etc. Exposure to crypto markets or market-making concepts. Who You Are You love solving analytical problems. You enjoy exploring datasets and finding patterns. You learn quickly and take ownership of your work. You’re excited to grow into trading research and quantitative modelling. Remote-first flexibility. Flexible payment options - choose to be paid in traditional currency (USD) or cryptocurrency (USDT / USDC). Exposure to real trading strategies and decision-making. Continuous mentorship from experienced quants and traders. Work on high-impact, low-latency trading infrastructure used across global crypto markets. Opportunity to grow as we scale rapidly across markets and product lines.

Responsibilities

The role involves analyzing market data, building statistical models, and applying machine learning methods to enhance trading strategies. You will also be responsible for backtesting and simulating trading strategies under various market conditions.