Senior Data Scientist - Global Internal Audit Mid-Platform

Responsibilities

Team Introduction: 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 data scientists and AI developers who will power our mission by building data products that enable and empower continuous auditing and the identification and discovery of risks throughout various verticals. You will be deploying your engineering, 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:

• Data Analytics Services: partner with auditors to provide data support and guidance for audit engagements, including conducting interviews, observing systems and operations, developing queries and testing strategies, deploying data quality checks to ensure completeness and accuracy for data sets, and deriving insights.
• Data Warehousing: develop and maintain data warehouses across different business verticals to efficiently support audit engagements; implement data quality checks for key data assets and continuously collaborate with data partners to maintain completeness and accuracy of these assets.
• Automation and self-service analytics: partner with auditors to identify and analyze key risk indicators, contribute to a continuous auditing data strategy that will translate into various use cases and corresponding data solutions that can automate the evaluation of the design and effectiveness of controls; build and maintain ETL data pipelines, as well as dashboards to support the solutions.
• AI Model Development and Implementation: design, develop, train, and implement machine learning and AI models, to solve audit, risk, and operational challenges.
• AI-Driven Automation and Insights: leverage machine learning and AI to automate business and audit processes, surface insights from unstructured and structured data, and extend the team’s ability to deliver actionable recommendations at scale.
• Data training and democracy: systematically organize the relationship between business processes, risks, and data, as well as provide comprehensive and meaningful data democracy to empower the audit team to derive insights.
• Stakeholder Relationships: develop and maintain collaborative working relationships with stakeholders, including data partners and owners across different business verticals.
• Professional Development: continue to develop and expand knowledge in data analytics practices, machine learning, AI, and ByteDance products through continuous education.

Qualifications

Minimum Qualifications:

• Bachelor’s degree or higher in a quantitative discipline such as Mathematics, Statistics, Computer Science, Financial Engineering, or Economics; familiarity with data warehouse concepts and large-scale processing frameworks (e.g., Hadoop, Flink).
• Strong proficiency in SQL and Python, including machine learning libraries like Hugging Face Transformers, TensorFlow, PyTorch, and scikit-learn.
• Solid experience with classical ML algorithms, large language model (LLM) architectures, and ML pipeline orchestration.
• Expertise in data integration, ETL processes, and large-scale data processing systems, including cloud platforms like AWS, GCP, Azure, or Snowflake.
• Proven track record building and maintaining data products or analytics solutions for continuous monitoring of KPIs, audits, and automated recurring analyses.
• Minimum of 3 years in applied data science, ML engineering, or AI research with LLMs and traditional ML models, plus 5+ years in data analytics, engineering, or science roles.
• Experience supporting or conducting AI/ML model audits and evaluations, with strong knowledge of lifecycle risks, controls, and governance.

Preferred Qualifications:

• Extensive knowledge of transformer-based LLM architectures (e.g., GPT, BERT, T5, PaLM) and classical ML algorithms (e.g., regression, tree-based methods, neural networks). Ability to analyze model design, training methods, data pipelines, and inference behaviors.
• Proficiency in frameworks for auditing models, including criteria like robustness, fairness, interpretability, alignment, and compliance. Familiarity with emerging LLM auditing methodologies such as LLMAuditor (probe generation/answering cycles, human-in-the-loop assessments).
• Expertise in defining and assessing model performance metrics (accuracy, precision/recall, F1, calibration, robustness, fairness metrics), measurement of hallucination rates in LLMs, bias/fairness quantification, confidence scoring, and stability analyses.
• Capability to identify model vulnerabilities including bias, fairness violations, harmful hallucinations, security risks, and to recommend remediation strategies.
• Front end and back end software development skills.