Description

The energy of a newsroom, the pace of a trading floor, the buzz of a recent tech breakthrough; we work hard, and we work fast, while keeping up the quality and accuracy we are known for. It is what keeps us inventing and reinventing, all the time. Our culture is wide open, just like our spaces. We bring out the best in each other through collaboration. Through our countless volunteer projects, we also help network with the communities around us. You can do amazing work here. Work you could not do anywhere else. It is up to you to make it happen.

Bloomberg’s Chief Risk Office plays a central role in ensuring that innovation is pursued responsibly across our global operations. As AI becomes increasingly embedded in Bloomberg’s products, platforms, internal workflows, and client-facing capabilities, the Chief Risk Office is building practical advisory capabilities that help technical and product teams identify, assess, mitigate, and monitor AI risks throughout the lifecycle.

What’s the role?

We are seeking a Senior AI Risk Technical Advisory Lead to serve as a senior subject matter expert for AI risk and responsible AI across Bloomberg. This person will work closely with engineering, product, data science, legal, compliance, CISO, privacy, and risk stakeholders to provide practical, technically credible guidance on AI use cases, AI systems and data-related controls, risk assessments of AI systems, risk/control evaluation of third-party AI solutions, and AI regulatory expectations (e.g., EU AI Act) and industry standards.

This role is designed for a senior practitioner who can bridge technical AI implementation and enterprise risk oversight. The person may have a matrixed or dual-reporting relationship to a technology or product-facing risk advisory leader to ensure strong connectivity with AI builders and front-line advisory teams, while maintaining alignment with the Chief Risk Office’s enterprise AI risk team.

We’ll trust you to:

Technical AI Risk Advisory

• Provide senior-level technical advisory support for AI and generative AI use cases across Bloomberg, including product, platform, internal tooling, automation, and third-party AI solutions.
• Evaluate AI risks related to bias, explainability, hallucination, robustness, model drift, data leakage, privacy, security, intellectual property, misuse, transparency, and human oversight.
• Advise teams on appropriate controls, testing, monitoring, documentation, and risk mitigation strategies based on use case, data sensitivity, model type, deployment context, and regulatory exposure.
• Review complex or higher-risk AI use cases and provide risk-based recommendations to governance forums and senior stakeholders.
• Help define practical technical standards for responsible AI development, testing, validation, deployment, monitoring, and retirement.

Framework and Control Development

• Partner with the Head of AI Risk Management to mature Bloomberg’s AI risk management framework, including classification, risk tiering, assessment methodology, control expectations, and monitoring standards.
• Translate external frameworks and regulatory expectations into practical internal control guidance for technical and product teams.
• Develop reusable advisory materials, playbooks, review criteria, technical checklists, and model documentation expectations.
• Support the development of risk indicators and monitoring approaches for AI systems, including performance, drift, hallucination, bias, security, user feedback, and issue trends.

Cross-Functional Partnership

• Partner deeply with Technology, Product, Data, Legal, Compliance, CISO, Privacy, Procurement, and business teams to address emerging AI risks.
• Facilitate technical risk discussions and help resolve questions where product goals, engineering constraints, legal obligations, and risk appetite intersect.
• Serve as a senior escalation point for complex AI risk questions and emerging responsible AI issues.
• Support AI risk governance forums, executive updates, regulatory inquiries, and internal reviews.

Enablement and Thought Leadership

• Serve as a trusted internal subject matter expert on responsible AI, technical AI risk, and AI control design.
• Develop and deliver training, guidance, and awareness materials for technical and non-technical stakeholders.
• Monitor developments in AI technology, AI regulation, industry standards, and emerging risk practices, and translate them into actionable program enhancements.

You’ll need to have:

• 10+ years of experience in AI/ML, data science, machine learning engineering, technology risk, model risk, security risk, data risk, or responsible AI.
• 4+ years of experience directly focused on AI/ML risk, AI governance, model governance, model validation, responsible AI, or technical advisory for AI systems.
• Strong technical understanding of AI/ML and generative AI systems, including model development, model evaluation, data pipelines, embeddings, retrieval-augmented generation, prompt engineering, monitoring, drift, robustness, explainability, and safety testing.
• Demonstrated ability to assess technical AI risks and recommend practical controls in complex enterprise environments.
• Hands-on familiarity with generative AI platforms and tools, open-source models, vector databases, or related AI infrastructure.
• Experience partnering with engineering, product, data science, legal, compliance, privacy, security, and risk stakeholders.
• Strong understanding of privacy, security, data governance, and regulatory issues relevant to AI systems.
• Excellent communication skills, including the ability to explain technical AI risks to senior stakeholders and translate governance expectations for technical teams.

We’d love to see:

• Experience working directly with AI/ML development teams on production systems.
• Experience in financial services, market data, media, enterprise technology, cloud, or other data-intensive environments.
• Familiarity with NIST AI RMF, ISO/IEC 23894, EU AI Act, OECD AI Principles, model risk management, or responsible AI frameworks.
• Experience with AI red teaming, model evaluation, bias testing, explainability tooling, monitoring tools, MLOps, LLMOps, or AI governance platforms.
• Experience advising on third-party AI tools, enterprise AI platforms, or AI-enabled vendor solutions.
• Certifications or advanced training in AI/ML, data science, privacy, information security, risk, or compliance.
• A pragmatic approach to enabling innovation while managing risk.