Senior Quantitative Analyst

About UsEstablished in March 2018, Bybit is one of the fastest growing cryptocurrency derivatives exchanges, with more than 70 million registered users. We offer a professional platform where crypto traders can find an ultra-fast matching engine, excellent customer service and multilingual community support. We provide innovative online spot and derivatives trading services, mining and staking products, as well as API support, to retail and institutional clients around the world, and strive to be the most reliable exchange for the emerging digital asset class.Our core values define us. We listen, care, and improve to create a faster, fairer, and more humane trading environment for our users. Our innovative, highly advanced, user-friendly platform has been designed from the ground-up using best-in-class infrastructure to provide our users with the industry's safest, fastest, fairest, and most transparent trading experience. Built on customer-centric values, we endeavour to provide a professional, 24/7 multi-language customer support to help in a timely manner.As of today, Bybit is one of the most trusted, reliable, and transparent cryptocurrency derivatives platforms in the space.About the RoleYou will own the end-to-end data analytics and modelling stack across Bybit's product lines and business units — from pipeline architecture to statistical modelling to AI-augmented workflow automation. This is a hands-on senior IC role that combines deep SQL/Python engineering with quantitative modelling and cross-functional influence.Key ResponsibilitiesDesign, build, and own production-grade data pipelines and transformation frameworks (SQL + Python) serving multiple product lines and BUs at scaleArchitect and maintain enterprise BI systems and reporting infrastructure; define and enforce standardized metrics logic to meet compliance and audit requirementsLead statistical and quantitative modelling for complex business scenarios — liquidity forecasting, competitive dynamics analysis, risk quantification, and market microstructure modellingDrive data quality strategy: build automated validation frameworks, anomaly detection systems, and data contract enforcement across upstream/downstream dependenciesDesign and implement AI agent workflows and LLM-powered tooling to accelerate data team productivity — from automated EDA to intelligent pipeline monitoring to natural language data accessPartner with product managers, engineering leads, and business stakeholders as the senior data voice — translate ambiguous business questions into rigorous analytical frameworks and actionable insightsMentor junior analysts and interns; establish best practices for code quality, documentation, and reproducibility across the data teamMajor Requirements5+ years of professional experience in data analytics, data engineering, or quantitative modelling rolesExpert-level SQL: complex query optimization, large-scale data modelling, warehouse design patterns (star schema, slowly changing dimensions, incremental processing)Expert-level Python: production data applications with pandas/numpy/scipy, pipeline orchestration (Airflow/Dagster/Prefect), package development and testingHands-on experience with big data stack at scale: Spark, Hadoop/Hive, or equivalent distributed processing frameworks (processing TB+ datasets)Proven experience with statistical modelling: regression, time series, causal inference, or simulation — applied to real business problems with measurable impactProficient with AI-powered development tools and agent workflows (Claude Code, Cursor, Copilot) — able to architect AI-augmented data workflows, not just use autocompleteStrong business acumen with demonstrated ability to independently scope, execute, and communicate complex analytical projects to senior stakeholdersTrack record of building systems and frameworks that scale beyond individual use — reusable pipelines, self-service tools, or internal platformsPreferred / BonusDeep understanding of crypto/exchange business logic: order books, matching engines, on-chain data, DeFi mechanics, or trading analyticsMachine learning engineering experience: model training, evaluation, deployment, and monitoring in productionExperience with RAG systems, knowledge base construction, or LLM fine-tuning for domain-specific applicationsBackground building AI agents or multi-step automated workflows for data operationsExperience with real-time data systems: streaming pipelines (Kafka/Flink), real-time dashboards, or event-driven architecturesContributions to open-source data tooling or published quantitative researchPrior experience at a crypto exchange, fintech, or high-frequency trading firm