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

Location:

San Jose

Employment Type:

Regular

Job Code: A174184

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.

• 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, 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.
• 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 use cases and data solutions that can automate the evaluation of the design and effectiveness of controls; build and maintain ETL data pipelines and dashboards.
• 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 company products through continuous education. Provide data training to empower the audit team to derive insights.

• 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 in data science or analytics from the technology sector.
• Hands‑on experience in designing, deploying, and monitoring large‑scale ML models with thorough understanding of lifecycle risks and controls; strong proficiency in SQL and Python (including Hugging Face Transformers, Tensor Flow, PyTorch, scikit‑learn) 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 (regression, tree‑based…