About The Team
Internal Audit is a global function responsible for providing independent assurance and evaluating the company's risk management, governance and internal control processes to determine if they are designed and operating effectively.
The Internal Audit team plans and executes audit projects according to our risk-based audit plan by evaluating financial, compliance, operational, and IT processes and controls.
We work with business functions in addressing risks and improving the control environment through timely and comprehensive audit work and tracking of remediation actions until completion.
We are looking for a data scientist who will power our mission by building data products that enable and empower continuous auditing and the identification and discovery of risks throughout various business domains , spanning product and technology.
You will be deploying your data analytics and data science skills to be part of the mission to build state-of-the-art analytics products for the audit team.
Responsibilities
- Partner with Internal Audit teams to design data-driven testing strategies for audits spanning e-commerce, ads), trust & safety, platform integrity, payments, infrastructure and security.
- Translate audit objectives, risk hypotheses, and control designs into scalable analytical tests, metrics, and models.
- Provide hands-on analytics support for audit engagements, including: -Understanding end-to-end business and product workflows -Conducting stakeholder interviews and system walkthroughs -Developing complex queries and analytical logic -Performing population completeness, accuracy, and integrity testing -Identifying anomalies, trends, and risk signals
- Define and monitor key risk indicators (KRIs) and risk-aligned metrics embedded within product and operational data.
- Analyze user behavior, monetization flows, content or transaction lifecycles, and system events to identify emerging or systemic risks.
- Bridge traditional audit concepts with modern data and product to surface insights not discoverable through manual testing.
- Data inventory and lineage: Develop a deep understanding of the company’s data ecosystem, including applications, event streams, operational databases, and data warehouses.
Identify and maintain an inventory of critical data assets relevant to audit and risk use cases;
assess data lineage, ownership, and reliability to support audit defensibility and repeatability.
- Data Warehousing and data foundations: Design, build, and maintain audit-ready data warehouses or data marts across multiple business verticals, ensuring performant, and well-modeled datasets.
Implement robust data quality checks, reconciliation logic, and monitoring to ensure the completeness and accuracy of key datasets used for audit and risk analytics.
- Contribute to the Internal Audit continuous auditing strategy, identifying opportunities to automate recurring audit procedures and control testing.
- Build and maintain ETL pipelines, reusable analytics frameworks, and dashboards that enable: ongoing monitoring of control effectiveness;
early detection of control breakdowns or abnormal patterns;
scalable reuse across audit engagements and regions.
- Where appropriate, leverage statistical methods, anomaly detection, or machine learning to enhance risk signal detection—while ensuring interpretability and auditability.
- Data Enablement & Analytics Democratization: systematically map relationships between business processes, risks, controls, and data to create reusable analytics assets;
enable self-service analytics for auditors by developing standardized datasets, documentation, and dashboards;
upskill audit team members through knowledge sharing, training sessions, and practical guidance on using data effectively in audits.
- Stakeholder Relationships: Develop and maintain collaborative working relationships with stakeholders, including data partners and owners across different business verticals.
Communicate complex analytical findings clearly to both technical and non-technical audiences, including senior leadership.
- Professional Development: Continue to develop and expand knowledge in data analytics practices, machine learning, AI, and company products through continuous education.