Machine Learning Quantitative Researcher - Crypto Derivatives

About Hytech

Hytech is a leading management consulting firm headquartered in Australia and Singapore, specialising in digital transformation for fintech and financial services organisations. We deliver end-to-end consulting services and provide robust middle- and back-office solutions that enable our clients to optimise operations, enhance efficiency, and stay ahead in a fast-evolving digital landscape. Our client portfolio includes top global trading platforms and leading crypto exchanges.

We apply AI and data-driven approaches to real-world financial use cases, including:

• Risk management
• Process optimization
• Decision support

Our focus is on delivering practical impact.

With more than 2,000 professionals worldwide, Hytech has a strong and growing international presence, with offices across Australia, Singapore, Malaysia, Taiwan, the Philippines, Thailand, Morocco, Cyprus, Dubai, and beyond.

Role Overview

Join our quantitative trading team to design, research, prototype, and deploy machine learning models for crypto derivative market-making and systematic trading strategies. You will collaborate closely with quantitative researchers, traders, and low-latency engineering teams to turn statistical and ML insights into production-ready trading systems. This role combines applied machine learning, time-series modelling, market microstructure research, and backtesting to identify alpha, improve quoting performance, optimise risk controls, and enhance execution quality across multiple CEX crypto venues.

Key Responsibilities

• Research, design, prototype, and validate machine learning models for market microstructure, order book dynamics, alpha signal generation, price prediction, spread optimisation, and execution cost modelling for crypto derivatives.
• Build rigorous backtesting, walk-forward validation, and out-of-sample testing pipelines to evaluate ML model performance; conduct robustness checks against overfitting, regime shifts, and exchange-specific quirks.
• Engineer feature sets from high-frequency market data, including order book snapshots, tick data, trade flows, funding rates, volume imbalance, queue dynamics, and latency metrics.
• Partner with traders and quant researchers to translate trading hypotheses into ML frameworks; iterate model design based on live market observations and strategy performance metrics.
• Deploy and integrate trained ML models into low-latency market-making and execution systems, working alongside systems engineers to balance model predictive power with latency constraints.
• Develop monitoring dashboards to track live model performance, feature drift, concept drift, prediction quality, and strategy PnL.
• Perform statistical research on market regimes, liquidity patterns, limit order behaviour, and trading halts to refine model assumptions and risk guardrails.
• Document research findings, model specifications, test methodologies, and production constraints for internal knowledge sharing and compliance review.
• Collaborate on stress testing and scenario simulation to test model behaviour under volatile market conditions, rate limits, and exchange outages.

Requirements

• Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Physics, Quantitative Finance, or a related quantitative field.
• 3+ years of hands-on experience building production machine learning models for systematic trading, market-making, or high-frequency finance (crypto or traditional markets preferred).
• Strong background in time-series ML, including gradient boosting, neural networks, sequence models, probabilistic modelling, anomaly detection, and feature engineering for high-frequency data.
• Proficiency in Python (NumPy, Pandas, Scikit-learn, PyTorch / TensorFlow) and solid statistical foundations (hypothesis testing, probability, Bayesian methods, correlation analysis).
• Deep understanding of market microstructure: order types, limit order books, funding, liquidity, rate limits, trading halts, and exchange API behaviour.
• Experience with backtesting frameworks, performance attribution, and handling overfitting in financial time series.
• Familiarity with streaming data, event-driven pipelines, Kafka / PubSub, or Redis streams for real-time feature ingestion.
• Experience working with low-latency trading stacks is a strong plus; ability to work with engineers to deploy models into C++/Rust production systems.
• Knowledge of crypto derivatives, perpetual futures, CEX market behaviour, and exchange edge cases is highly preferred.
• Strong coding hygiene, experimental rigour, and ability to clearly communicate complex quantitative results to non-ML team members (traders, engineers).

Nice-to-Have

• Experience with online learning, reinforcement learning for market making / order execution.
• Hands-on experience with low-latency profiling, performance tuning, and cloud containerised deployments (Docker).
• Publication or open-source contributions related to time-series forecasting or quantitative finance.
• Experience building risk models using ML for real-time position monitoring.