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

Requirements

• Proficiency in frameworks for auditing models, including criteria like robustness, fairness, interpretability, alignment, and compliance.
• 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.
• 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.
• 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.
• Hands-on experience in designing, deploying, and monitoring large-scale ML models with thorough understanding of lifecycle risks and controls.
• 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.
• 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.

Benefits

• Generous Paid Time Off
• 401k Matching
• Retirement Plan