Btse3mo ago

Quant Researcher

Hong KongRemoteFull-timemid

OtherQuant Researcher

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Quick Summary

Overview

About BTSE: BTSE Group is a global leader in fintech and blockchain technology, anchored by three core business pillars: Exchange, Payments, and Infrastructure Development. Serving over 100 corporate clients worldwide, we provide white-label exchange and payment solutions.

Technical Tools

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About BTSE:

BTSE Group is a global leader in fintech and blockchain technology, anchored by three core business pillars: Exchange, Payments, and Infrastructure Development. Serving over 100 corporate clients worldwide, we provide white-label exchange and payment solutions. Our offerings encompass everything from exchange infrastructure hosting and development to custody, wallets, payments, blockchain integration, trading, and more. We are looking for talented professionals in marketing, operations, customer support, and other departments. The roles offered may be on-site, remote, or hybrid, in collaboration with our local partner.

About the opportunity:

We are seeking a highly skilled Quant Developer to join our trading system development team. You will play a critical role in building, optimising, and maintaining a high-performance, low-latency trading system. This is an exciting opportunity to work in a fast-paced, collaborative environment and make a direct impact on trading strategies and operations.

Design, research, and validate systematic alpha factors across price, order book, funding, flow, and microstructure data

Build and maintain a structured alpha research pipeline (data → feature → signal → evaluation → iteration)

Conduct factor analysis including IC, IR, decay, stability, regime sensitivity, and turnover analysis

Collaborate with engineering teams to ensure research outputs are production-ready

Continuously iterate and improve existing alpha signals, even if historical performance has decayed

Explore AI-assisted research workflows for factor generation, feature selection, and hypothesis exploration (bonus)

3+ years of quantitative research experience in systematic trading, alpha research, or related fields

Strong proficiency in Python, with hands-on experience using Jupyter Notebook as a primary research environment

Solid understanding of the end-to-end alpha research process, including: Data cleaning & normalization, Feature engineering, Factor construction, Signal evaluation & validation.

Have built and operated a complete alpha research framework (personal or professional)

Proven experience discovering alpha factors with strong historical predictive power, e.g.: 1. Information Coefficient (IC) consistently above 0.05–0.1 on daily frequency or higher IC on lower-frequency signals with reasonable stability (factors that later decayed are acceptable, as long as the original research process was sound)

Strong analytical thinking and ability to explain why a factor works, not just that it works

Experience using AI / ML models (e.g. tree models, neural networks, representation learning) for alpha research

Hands-on experience with local deployment of AI models (not just calling APIs)

Familiarity with AI-assisted factor discovery workflows (feature generation, signal screening, regime detection, etc.)

Background in crypto, derivatives, or high-frequency / microstructure-driven markets