Senior IT Architect II (AI Product Manager)

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As the AI Product Manager, you will be responsible for driving the product strategy and execution for AI-powered products within Enterprise Information Technology (EIT), with a strong emphasis on governance and ethical considerations. You will work closely with cross-functional teams to deliver AI solutions that meet the needs of Finance, Legal, Security, Privacy, and HR functions, as well as enterprise-wide AI products, including those utilizing Large Language Models (LLMs). You will also play a key role in implementing Nielsen's AI Literacy Program in accordance with EU AI Act guidelines and collaborate with business functions on AI governance.

Role

You will be responsible for defining and driving the strategy, roadmap, and execution of AI-powered products for EIT, ensuring alignment with Nielsen's overall business goals and adherence to AI governance principles. You will manage the entire product lifecycle, from ideation to launch and iteration, while fostering collaboration across diverse teams and promoting AI literacy throughout the organization.

Responsibilities

• AI Product Strategy for EIT: Define the product vision, strategy, and roadmap for AI-powered products within EIT, with a focus on Finance, Legal, Security, Privacy, and HR functions, including the integration of LLMs. Align these strategies with Nielsen's overall business goals, with a strong emphasis on governance and ethical considerations.

• AI Product Lifecycle Management (EIT): Drive the end-to-end product lifecycle for EIT AI products, from ideation and requirements gathering to development, launch, and iteration. Ensure timely and successful delivery of AI solutions that meet the specific needs of EIT functions, while adhering to AI governance policies.

• Cross-Functional Collaboration (EIT): Collaborate closely with data scientists, engineers, designers, and business stakeholders within EIT to translate business needs into AI product requirements. Ensure seamless integration of AI capabilities, including LLMs, into EIT workflows and systems, with a focus on compliance, security, and ethical use.

• AI Use Case Identification (EIT): Identify and evaluate potential AI use cases within EIT functions, including opportunities to leverage LLMs to improve efficiency, decision-making, and user experience. Assess the feasibility, viability, and alignment of these use cases with EIT objectives and governance frameworks.

• AI Product Requirements (EIT): Define and prioritize AI product requirements for EIT, including data needs, model specifications (especially for LLMs), performance metrics, and user experience considerations. Ensure that requirements address specific EIT concerns, such as data privacy, security, legal compliance, and auditability.

• Ethical AI and Governance (EIT): Integrate ethical considerations and governance principles into the product development process for EIT AI products. Ensure that AI systems, including those utilizing LLMs, are developed and deployed responsibly, transparently, and in compliance with relevant regulations, with a strong emphasis on mitigating risks related to bias, fairness, and explainability in the EIT context.

• Performance Monitoring and Optimization (EIT): Establish metrics and processes to monitor the performance, effectiveness, and user adoption of AI products within EIT. Drive continuous improvement and optimization, with a focus on ensuring that AI systems meet EIT-specific performance, compliance, and security requirements.

• AI Literacy Program Implementation: Develop and implement an AI Literacy Program for Nielsen, aligned with EU AI Act guidelines. This includes creating training materials, delivering training sessions, and promoting AI awareness across the organization, with a focus on educating EIT staff on the responsible and ethical use of AI, including LLMs.

• Collaboration with Business Functions (Governance Perspective): Collaborate with Nielsen's business functions (outside of EIT) to ensure alignment of business AI product development with the overall AI governance framework. Provide guidance and support to business units on how to incorporate governance principles into their AI initiatives.

• Stakeholder Communication: Communicate product vision, roadmap, and progress to internal and external stakeholders, including senior management, EIT leadership, and relevant business units. Ensure clear and consistent communication regarding AI governance policies and their impact on product development.

Measurement

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

• Values: Open, Connected, Useful, Personal

What does Success mean for this role?

A successful AI Product Manager will drive the development and adoption of AI products across EIT, ensuring alignment with business goals, user needs, and AI governance principles. They will effectively manage the product lifecycle, foster cross-functional collaboration, and promote AI literacy throughout the organization. Their work will result in the delivery of high-quality, ethical, and compliant AI solutions that enhance EIT functions and contribute to Nielsen's overall success in the AI space.

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 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 product management, with a focus on AI or machine learning products.

• Proven track record of successfully launching and managing AI products through the entire product lifecycle.

• Strong understanding of machine learning concepts, algorithms, and technologies, including LLMs.

• Experience working with cross-functional teams, including data scientists, engineers, and designers.

• Familiarity with AI regulations, such as the EU AI Act, and data privacy regulations, such as GDPR.

• Know-How

• See Know-How Assessment Tool

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