Btse2mo ago

Quant Researcher

Taipei · TaipeiFull-timemid

OtherQuant Researcher

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Overview

About BTSE: 彼特思方舟 is a specialized service provider dedicated to delivering a full spectrum of front-office and back-office support solutions,

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OtherQuant Researcher

About BTSE:

彼特思方舟 is a specialized service provider dedicated to delivering a full spectrum of front-office and back-office support solutions, each of which are tailored to the unique needs of global financial technology firms. 彼特思方舟 is engaged by BTSE Group to offer several key positions, enabling the delivery of cutting-edge technology and tailored solutions that meet the evolving demands of the fintech industry in a competitive global market.

BTSE Group is a leading global fintech and blockchain company that is committed to building innovative technology and infrastructure. BTSE empowers businesses and corporate clients with the advanced tools they need to excel in a rapidly evolving and competitive market. BTSE has pioneered numerous trading technologies that have been widely adopted across the industry, setting new benchmarks for innovation, performance, and security in fintech. BTSE’s diverse business lines serve both retail (B2C) customers and institutional (B2B) clients, enabling them to launch, operate, and scale fintech businesses. BTSE is seeking ambitious, motivated professionals to join our B2C and B2B teams.

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