Senior Manager Internal Audit (Audit Analytics, Automation & Innovation

Established in 2018, Bybit is one of the world’s leading cryptocurrency exchanges and digital financial platforms, serving over 80 million users across more than 200 countries and regions. Powered by world-class technology and a user-first mindset, Bybit delivers a seamless ecosystem across trading, payments, wealth management, custody, institutional services, and Web3 — connecting users to the future of digital finance.

Our core values define how we build. We listen, care and improve to create products and experiences that put users first. Backed by a global team of ambitious builders, problem-solvers, and innovators, we foster a high-performance and fast-moving environment where talent is empowered to drive real impact at the global scale. Supported by 24/7 multilingual customer service and a strong commitment to innovation, we are shaping the future of finance through technology, collaboration, and bold execution.

Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services.

We are seeking a data analytics specialist to join our internal audit function and drive the team's analytics, automation, and technology innovation capability. This role will lead the design and execution of data-driven audit solutions — from fraud surveillance and continuous monitoring to automation of audit procedures — with a strong focus on operational risk and proactive detection.

The ideal candidate will bridge the gap between data engineering and audit methodology — able to translate business and operational risks into structured analytical approaches and present complex data findings in clear, actionable language for non-technical stakeholders.

• Lead Internal Audit's data analytics, automation, and technology innovation initiatives, including the ongoing development and execution of the department's technology roadmap.
• Design, build, and maintain SQL-based analytical models to support audit engagements — including population testing, anomaly detection, trend analysis, and control effectiveness assessment.
• Analyse data to identify and implement analytics, automation, and reporting solutions that provide meaningful insight and improve the effectiveness of audit planning and execution across the audit universe.
• Embed analytics into audit methodology and related processes, including risk assessment, audit planning, scoping, fieldwork, exception analysis, issue impact quantification, corrective action validation, and reporting.
• Drive automation opportunities that streamline audit procedures, improve consistency, and reduce manual effort across Internal Audit activities.
• Transform raw data from multiple sources (data warehouse, blockchain, internal systems) into audit-ready datasets and quantified findings.
• Provide data-driven evidence and quantitative analysis to support audit conclusions, replacing or supplementing traditional sample-based testing with full-population analytics.
• Collaborate with Business Audit Director to design and operate the Fraud Risk Radar programme — a proactive fraud surveillance framework that detects suspicious employee wallet activity, kickback patterns, dual employment signals, and affiliate commission manipulation in near real-time.
• Develop and maintain continuous monitoring dashboards and automated alert systems to detect control failures, policy violations, and emerging operational risks.
• Define monitoring rules and thresholds in collaboration with audit leads, calibrated to the company's risk appetite and operational context.
• Investigate and triage alerts, escalating confirmed exceptions to audit or investigation teams with supporting data packages.
• Support forensic and ad-hoc investigations with rapid data extraction, pattern analysis, and evidence packaging.

AI & Advanced Analytics

• Apply advanced analytical techniques (anomaly detection, clustering, predictive modelling) to identify patterns, outliers, and emerging risks across business operations.
• Explore and implement AI/ML-driven tools to enhance audit efficiency — including…