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

Responsibilities

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.

• 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 at scale. Develop, 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 company products through continuous education. Provide data training to empower the audit team to derive insights.

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, TensorFlow, 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 scoring, and stability analyses.
• 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).
• Working knowledge of classical ML algorithms and LLM architecture and deep technical expertise in LLMs and Traditional ML and a proven track record supporting or performing AI/ML model audits or evaluations within a corporate, regulatory, or advisory context.
• High oral, written, reading and listening proficiency in Mandarin is required due to system use, technical documents and frequent communication with Chinese stakeholders.

• PhD degree in a quantitative discipline, such as Mathematics, Statistics, Computer Science, Financial Engineering, Operations Research, or Economics.
• Proficiency in frameworks for auditing models, including criteria like robustness, fairness, interpretability, alignment, and compliance. Familiarity with emerging LLM auditing methodologies such as LLM Auditor (probe generation/answering cycles, human‑in‑the‑loop assessments).
• Ability to analyze model design, training methods, data pipelines, and inference behaviors.
• Capability to identify model vulnerabilities including bias, fairness violations, harmful hallucinations, security risks, and to recommend remediation strategies.
• Experience building and maintaining data analytics solutions for continuous audit programs, including automating common analyses and recurring checks plus the ability to clearly communicate technical findings, risk assessments, and recommendations to technical and non‑technical stakeholders.
• Experience with data integration, ETL processes, and large‑scale data processing systems plus working knowledge of cloud‑based infrastructure such as AWS, GCP, Azure or Snowflake; working knowledge of large scale data processing techniques, such as Hadoop, Flink and MapReduce and a good understanding of data warehouse and data modeling principles.
• Front end and back end software development skills.

About TikTok

TikTok is the leading destination for short‑form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us

Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.

We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an “Always Day 1” mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

TikTok Accommodation

TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at

Job Information

For Pay Transparency Compensation Description (Annually)

The base salary range for this position in the selected city is $123,120 - $281,580 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short‑term and long‑term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates: Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

• Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
• Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
• Exercising sound judgment.