Senior Data Scientist, Model Risk & Data Analytics, Internal Audit - AMS

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 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 - 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).

- Model Evaluation & Audit Frameworks: conduct audits on the model lifecycle from training through deployment and monitoring, ensuring compliance with quality, performance, fairness, and risk-management standards.

- Risk Identification & Mitigation:
Identify model vulnerabilities including bias, fairness violations, harmful hallucinations, security risks, and recommend remediation strategies.

- Measurement Metrics & Statistical Validation:
Define and assess 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.

- Communication &

Collaboration:

Develop and maintain collaborative working relationships with stakeholders, including data partners and owners across different business verticals. Clearly communicate technical findings, risk assessments, and recommendations to technical and non-technical stakeholders.

- 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-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 elop, train, and implement proprietary machine learning and AI models, to scale up audit testing insights.

- Professional Development:
Continue to develop and expand knowledge in data analytics practices, machine learning, AI, and Byte Dance products through continuous education. Provide data training to empower the audit team to derive insights.

Minimum Qualifications - Bachelor's degree in a quantitative discipline, such as Mathematics, Statistics, Computer Science, Financial Engineering, Operations Research, or Economics.

- Minimum of 5 years professional experience in applied data science, machine learning engineering, or AI research, specifically working with LLMs and traditional ML models and at least 5 years practical experience of data science or analytics from the technology sector, including but not limited to B2C SaaS, media tech, e-commerce, social media platforms, fintech etc.

- Hands-on experience in designing, deploying, and monitoring large-scale ML models with thorough understanding of lifecycle risks and controls plus strong proficiency in SQL and Python (including libraries such as Hugging Face Transformers, Tensor Flow, PyTorch, scikit-learn), data analysis tools, and ML pipeline orchestration platforms.

- 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…