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Role 

As an ML Engineer reporting to the Director of AI Governance, you will play a critical role in implementing and operationalizing Nielsen's AI Governance framework. You will be responsible for ensuring that our AI systems are developed and deployed ethically, responsibly, and in compliance with relevant regulations and industry best practices. Your work will directly support the business value, trust, and transparency of our AI-driven products and services.

Responsibilities

• Implementing AI Governance Framework: You will translate the principles and guidelines set forth by the Director of AI Governance into practical technical standards and implementation strategies within the ML lifecycle. This includes defining coding standards, documentation requirements, and deployment protocols that adhere to governance policies.

• Ensuring Technical Compliance and Guardrails: A significant part of your role involves implementing and validating technical guardrails within ML models and pipelines. This includes embedding fairness checks, privacy-preserving techniques, and security measures directly into the AI systems you build and maintain. You'll work to ensure that AI outputs are explainable, auditable, and aligned with ethical guidelines.

• Conducting AI Risk Assessments (Technical Focus): You will contribute to AI risk assessments by providing deep technical insights into potential risks associated with specific ML models, algorithms, and data pipelines. This involves identifying vulnerabilities, biases, and potential for unintended consequences from a technical standpoint and proposing mitigation strategies.

• Facilitating AI Use Case Approval Workflow: You will play a technical role in the AI use case approval process. This might involve providing technical documentation, assessing the feasibility and risks associated with a proposed use case from an ML perspective, and ensuring that the technical implementation aligns with the approved parameters.

• Monitoring and Reporting on AI Performance and Compliance: You will be responsible for setting up monitoring systems to track the performance, fairness, and compliance of deployed ML models against the defined governance metrics. You'll generate regular reports for the Director of AI Governance, highlighting any deviations, potential risks, or areas needing attention from a technical standpoint.

• Defining Practical Technical Standards: You will contribute to the definition and documentation of practical technical standards and best practices for AI development, ensuring that these standards are aligned with the broader AI Governance framework and are implementable by the wider ML engineering team.

• Validating AI Guardrails Implementation: You will be responsible for testing and validating that the implemented AI guardrails are functioning as intended and effectively mitigating identified risks. This involves developing and executing test cases and documenting the validation process.

• Supporting Global AI Regulatory Compliance Strategy (Technical Input): You will provide technical expertise and support to the Director of AI Governance in understanding and implementing global AI regulatory requirements. This might involve researching technical implications of new regulations and contributing to compliance strategies from an ML engineering perspective.

• Building Accelerators for AI Governance: You will be responsible for designing, developing, and deploying tools, scripts, or automated workflows (accelerators) to streamline routine AI Governance tasks. This could include automating compliance checks, risk assessment processes, or the generation of governance-related documentation.

• Developing APIs for Model Governance: You will design and build APIs that expose key governance-related functionalities for ML models. This could involve APIs for querying model metadata, accessing audit logs, triggering compliance checks, or retrieving fairness metrics, making governance more integrated into the model lifecycle and accessible to other systems.

• Collaborating with Cross-Functional Teams: You will work closely with data scientists, product managers, legal, and ethics teams to ensure that AI initiatives are developed and deployed responsibly and in accordance with governance policies. Your ability to translate technical complexities into understandable terms for non-technical stakeholders is crucial.

• Promoting AI Governance Awareness: You will help promote awareness and understanding of AI governance principles and practices within the ML engineering team and contribute to training and knowledge-sharing initiatives.

Measurement

• Leadership: Curious, Collaborative, Inclusive, Proactive, Accountable, Committed

• Values: Open, Connected, Useful, Personal

What does Success mean for this role?

A successful ML Engineer in AI Governance will effectively implement and operationalize AI governance principles within the organization. They will ensure that AI systems are developed and deployed in a compliant, ethical, and responsible manner, contributing to building trust and mitigating risks. Their technical expertise and collaboration will be essential in enabling the business to leverage AI technologies with confidence and integrity.

Organization

• Functional Reporting Lines (hierarchical, direct manager and reports):

• Manager: Director - AI Governance

• Reports: [N/A]

• Networks (internal & external links and networks)

• Internal:

• VP, Strategy and Architecture

• VP, Cloud, Infra, AI & Emerging Tech Enablement

• Data Science and Product teams (Audience Measurement, Gracenote, and Analytics Portfolio)

• Leaders in HR, Legal, and Finance

• Nielsen Legal, Privacy, and Cybersecurity

• Enterprise IT colleagues

• Internal Audit team

• External:

• Vendors and partners for AI solutions

• Industry analysts for research and best practices

• External Audit or Consultants as required

Requirements

• Education & Training

• Bachelor's degree in Computer Science, Information Management, or a related field.

• This role is applicable across various industries, but experience in regulated industries (e.g., finance, healthcare) is a plus.

• Fluency in English and local language mandatory

• Experience

• Excellent communication, collaboration, and leadership skills.

• Ability to work effectively with cross-functional teams and influence stakeholders.

• High levels of energy, conscientiousness, rapport building, resiliency, inventiveness, and curiosity.

• Strong understanding of ethical considerations and the societal impact of AI.

• Ability to stay informed about emerging trends and best practices in AI governance.

• Experience with data & analytics governance tools and technologies.

• Ideally, 5+ years of experience in machine learning engineering, with a focus on responsible AI development and deployment.

• Proven track record of building and deploying machine learning models and pipelines in a production environment.

• Strong programming skills in Python and experience with relevant ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

• Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).

• Experience in developing APIs and automation tools.

• Know-How

• See Know-How Assessment Tool

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