About BTSE:BTSE Group is a global leader in fintech and blockchain technology, anchored by threecore business pillars: Exchange, Payments, and Infrastructure Development. Servingover 100 corporate clients worldwide, we provide white-label exchange and paymentsolutions. Our offerings encompass everything from exchange infrastructure hostingand development to custody, wallets, payments, blockchain integration, trading, andmore.We are looking for talented professionals in marketing, operations, customer support,and other departments. The roles offered may be on-site, remote, or hybrid, incollaboration with our local partner.About the opportunity:We are seeking a highly skilled Quant Developer to join our trading system developmentteam. 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 afast-paced, collaborative environment and make a direct impact on trading strategiesand operations.ResponsibilitiesDesign, research, and validatesystematic alpha factorsacross price, order book, funding, flow, and microstructure dataBuild and maintain astructured alpha research pipeline(data → feature → signal → evaluation → iteration)Conduct factor analysis includingIC, IR, decay, stability, regime sensitivity, and turnover analysisCollaborate with engineering teams to ensureresearch outputs are production-readyContinuously iterate and improve existing alpha signals, even if historical performance has decayedExploreAI-assisted research workflowsfor factor generation, feature selection, and hypothesis exploration (bonus)Requirements3+ years of quantitative research experiencein systematic trading, alpha research, or related fieldsStrong proficiency inPython, with hands-on experience usingJupyter Notebookas a primary research environmentSolid understanding of theend-to-end alpha research process, including: Data cleaning normalization, Feature engineering, Factor construction, Signal evaluation validation.Have built and operated acomplete alpha research framework(personal or professional)Proven experience discovering alpha factors withstrong historical predictive power, e.g.: 1.Information Coefficient (IC)consistently above0.05–0.1on 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 explainwhy a factor works, not just that it worksNice to haveExperience usingAI / ML models(e.g. tree models, neural networks, representation learning) for alpha researchHands-on experience withlocal 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#LI-MC1