Senior Data Scientist: Global Data Analytics - Internal Audit

About the role

Internal Audit is a global function that provides independent assurance on the company's risk management, governance and internal control processes. It plans and executes audit projects based on a risk-based plan, covering financial, compliance, operational and IT processes. ByteDance is looking for a Senior Data Scientist to build data products that enable continuous auditing and the discovery of risks across product and technology domains.

Key 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.
• Support audit engagements with business workflow understanding, stakeholder interviews, complex queries, population testing and anomaly detection.
• Define and monitor key risk indicators and risk-aligned metrics, and analyze user behavior, monetization flows and system events to spot emerging risks.
• Build and maintain audit-ready data warehouses or data marts, with data quality checks, reconciliation logic and monitoring.
• Develop ETL pipelines, reusable analytics frameworks and dashboards, and contribute to automating recurring audit procedures.
• Use statistical methods, anomaly detection or machine learning where appropriate, while keeping results interpretable and auditable.
• Create reusable analytics assets, standard datasets and dashboards, and enable self-service analytics and training for auditors.
• Communicate analytical findings clearly to technical and non-technical audiences, including senior leadership.

Required profile

• Bachelor's degree or above in a quantitative discipline such as Mathematics, Statistics, Computer Science, Financial Engineering, Operations Research or Economics.
• Extensive hands-on experience in data analytics, data science, analytics engineering or data engineering roles.
• Proven experience building data products in product analytics, business/marketing analytics, risk analytics, compliance analytics or continuous audit analytics.
• Ability to turn ambiguous business or risk questions into structured analytical approaches.

Required skills

• Strong SQL and at least one mainstream programming language (Python or R).
• Solid understanding of ETL, data integration and large-scale data processing, with cloud infrastructure (AWS, GCP, Azure, Snowflake).
• Experience with data quality checks, reconciliations or data observability solutions.
• Preferred: Hadoop, Flink and MapReduce; data warehousing, dimensional modeling and analytics engineering; federated data environments; stakeholder management and data storytelling.