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Senior Data Scientist, Model Risk & Data Analytics, Internal Audit - AMSEllis Technologies, Inc. • Los Angeles, California, United States
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Senior Data Scientist, Model Risk & Data Analytics, Internal Audit - AMS

Ellis Technologies, Inc.
  • US
    Los Angeles, California, United States
  • US
    Los Angeles, California, United States
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Über

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 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
Minimum Qualifications:
Bachelor's degree in a quantitative discipline, such as Mathematics, Statistics, Computer Science, Financial Engineering, Operations Research, or Economics.
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.
Preferred Qualifications:
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.
Job Information The base salary range for this position in the selected city is $129,960 - $277,970 annually.
Benefits 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).
Equal Employment Opportunity Statement 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: 1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues; 2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and 3. Exercising sound judgment.
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  • Los Angeles, California, United States

Sprachkenntnisse

  • English
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