Risk Analytics-Python-Senior

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. Job Title: Risk Analytics – Internal Audit Analyst Experience: 4-6 years

: EY is a leading global professional services firm offering a broad range of services in assurance, tax, transaction, and advisory services. At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. As part of EY’s Risk Consulting practice, you will contribute to Risk Analytics and risk-driven AI solutions that support areas such as financial risk, regulatory compliance, internal controls, audit analytics, fraud detection, and enterprise risk management. Our mission is to design, build, and integrate scalable, secure, and auditable analytics platforms that enable clients to proactively identify, assess, and manage risk.

: • Expert-level proficiency in Python programming and software design principles. • Experience designing risk analytics pipelines, including data ingestion, transformation, validation, and monitoring. • Awareness of risk, controls, compliance, fraud, and audit analytics concepts. • Strong experience in data engineering in Optro (formerly known as AuditBoard) and distributed systems. • Hands-on experience with microservices architecture and CI/CD pipelines. • Proficiency in SQL, database performance tuning, and data governance. • Familiarity with cloud platforms (Azure preferred, AWS/GCP optional).

: • Strong understanding of AI Governance frameworks. • Knowledge of ML Ops practices and tools (Kubeflow, Airflow, ML Flow). • Exposure to big data ecosystems (Hadoop, Spark). • Experience with containerization (Docker, Kubernetes). • Strong understanding of data security and compliance in financial services. • Good working knowledge of Alteryx and Power BI is preferred.