New opportunity

Head of AI

Janus Henderson Investors · New York, United States

About this role

The Head of AI will lead Janus Henderson Investors’ firm-wide AI strategy and transformation in its asset-management business. Reporting to the CEO as a member of senior leadership, the role partners with executive, innovation and technology teams and engages regularly with the Board of Directors and legal entity and fund boards. Its purpose is to embed AI ethically and effectively across investment research, client experiences, products and operations.

Responsibilities

Develop and execute an AI and technology strategy that delivers near-term results and longer-term transformation. Lead AI research, development, innovation initiatives and pilots; evaluate emerging AI techniques, fintech developments and regulatory changes for business value and return on investment.
Build and mentor a high-performing AI and technology team. Advocate for AI adoption with internal and external stakeholders and collaborate with investment, technology, finance, risk, compliance and client teams.
Establish standards for AI product development and use, including responsible AI, data quality, governance, security, privacy and regulatory compliance. Guide scalable, secure technical infrastructure and work with data engineering and cybersecurity teams to protect firm and client data.
Manage relationships with AI technology providers and develop the AI and technology budget, allocating resources against strategic priorities.

Required qualifications

A master’s degree in Computer Science, Data Science, Statistics, Engineering or a related quantitative field; 10+ years in AI strategy and transformation with related technology roles, including at least 5 years in leadership or management. Candidates must demonstrate senior transformation leadership, organization-wide AI adoption, AI R&D and product delivery, team development, executive communication and cross-functional influence. The role requires an understanding of software development, system architecture and cybersecurity, plus familiarity with AI ethics and data-security methods such as bias detection, LIME, SHAP, anonymization, encryption and secure data enclaves.

The New York role offers hybrid working. The estimated base salary range is $250,000–$300,000; actual pay may differ. The position may qualify for a discretionary annual bonus. Listed benefits include holiday, health and wellbeing programs, retirement plans, paid volunteer time, family leave and professional-development support.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI strategyAI transformationAI governanceResponsible AIAI ethicsAI research and developmentStrategic leadershipTeam leadershipStakeholder managementData governanceData securityData privacyCybersecurityRisk managementRegulatory complianceSystem architectureSoftware developmentBias detectionModel explainabilityLIMESHAPData anonymizationEncryptionSecure data enclavesBudget managementFinancial planningVendor management

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Set a firm-wide AI strategy
The posting calls for a cohesive AI and technology strategy that delivers near-term impact and longer-term transformation. Prioritize opportunities across investment research, client experiences, products and operations, using business value and ROI to guide decisions.
Turn innovation into adoption
Lead AI research, development and pilots, assess emerging techniques and fintech, and support organization-wide adoption. The qualifications emphasize bringing AI-driven products to market, not just testing ideas.
Establish responsible-AI controls
Set standards for AI product development and use, including data quality, governance, explainability, security, privacy and regulatory compliance. Partner with data engineering and cybersecurity to protect firm and client data.
Align executive and functional leaders
The role reports to the CEO, engages regularly with the Board of Directors and legal entity and fund boards, and works across investment, technology, finance, risk, compliance and client teams. Clear recommendations and cross-functional influence are central to execution.
Build delivery capacity and allocate resources
Build and mentor the AI and technology team; guide scalable, secure infrastructure; manage provider relationships and the AI and technology budget. The posting ties resource allocation to strategic priorities, ROI and efficiency.

Likely challenges to think through

Balance early results with durable foundations
Preparation hypothesis: pressure for visible AI outcomes could compete with investment in data quality, infrastructure and controls. Prepare a phased approach that distinguishes pilot success from readiness to scale and explains how each stage earns further investment.
Make sensitive-data use both valuable and defensible
Preparation hypothesis: personalized client solutions and investment insights may create tension between richer data use and privacy, security, explainability and regulatory obligations. Prepare to describe decision criteria for data access, model evaluation and safeguards without assuming a particular firm policy.
Build consensus without losing momentum
Preparation hypothesis: decisions spanning executives, boards and several specialist teams may involve competing priorities. Prepare an example framework for presenting options, evidence, risks and ownership so stakeholders can make timely, accountable decisions.

Practice your answers

Technical practice

How would you sequence a firm-wide AI strategy to deliver near-term value and longer-term transformation?

What to coverThe role calls for a cohesive AI and technology strategy across the firm. Preparation suggestion: Explain how you would prioritize opportunities, set measurable outcomes and revisit the roadmap as evidence and risks change.

How would you evaluate an AI use case for investment research versus a client-facing experience?

What to coverThe posting identifies investment research and client experiences as areas for AI adoption. Preparation suggestion: Compare the value, data quality, human oversight and potential client impact you would assess in each case.

When should an AI research initiative move from pilot to product?

What to coverThe role leads AI R&D, pilots and product delivery, with attention to business value and ROI. Preparation suggestion: Describe decision criteria for testing, stopping or scaling an initiative, including evidence of usefulness and readiness for controlled use.

What standards would you set for responsible AI product development and use?

What to coverThe posting calls for consistent quality and ethical integrity, and names bias detection and explainability techniques such as LIME and SHAP. Preparation suggestion: Address appropriate testing, documentation, oversight and the limits of explanations when assessing model behavior.

How would you govern the quality and integrity of data used across AI systems?

What to coverThe role oversees data governance in partnership with data engineering teams. Preparation suggestion: Discuss how ownership, validation, access controls and monitoring could support accurate, secure and compliant use of data.

How would you protect sensitive client data during model development?

What to coverThe posting names anonymization, encryption and secure data enclaves as relevant methods. Preparation suggestion: Explain what each protects, where residual exposure may remain and how you would verify that controls work in practice.

What architecture considerations matter when scaling AI across the firm?

What to coverThe role guides robust, scalable and secure infrastructure and requires understanding of software development and system architecture. Preparation suggestion: Discuss integration, reliability, access control and maintainability, including trade-offs between rapid deployment and durable systems.

How would you assess and respond to cybersecurity risks introduced by AI systems?

What to coverThe posting calls for protection against internal and external threats and continuous engagement with cybersecurity teams. Preparation suggestion: Outline how you would identify exposures, assign controls and monitor whether those controls remain effective.

How would you evaluate an AI technology provider?

What to coverThe role manages relationships with AI providers to enhance solutions and accelerate innovation. Preparation suggestion: Compare strategic fit, technical integration, data protection, reliability and cost before recommending a provider or continued investment.

How would you allocate an AI budget as technologies and regulations change?

What to coverThe posting calls for budget management, ROI assessment and monitoring of AI techniques, fintech developments and regulatory changes. Preparation suggestion: Explain how you would balance established initiatives against experiments, and what evidence would trigger reallocation.

Behavioural practice

Tell me about a time you explained a consequential technology strategy to senior decision-makers.

What to coverThe role reports to the CEO and engages with boards. Preparation suggestion: Use a real situation/action/result example showing how you conveyed trade-offs, answered challenges and established a clear decision or next step.

Describe a time you aligned teams with competing priorities around a technology change.

What to coverThe posting emphasizes partnership across investment, technology, finance, risk, compliance and client teams. Preparation suggestion: Explain the situation, your actions to surface and resolve differences, and the resulting decision or outcome.

Tell me about a time you built or developed a team while delivering a demanding initiative.

What to coverBuilding, mentoring and developing AI and technology leaders are stated responsibilities. Preparation suggestion: Give a situation/action/result example that distinguishes how you supported individual growth from how you delivered the team objective.

Describe a time you helped an organization adopt an unfamiliar technology.

What to coverThe role champions organization-wide AI adoption with internal and external stakeholders. Preparation suggestion: Show the initial concerns, the actions you took to build understanding and the observable result, without assuming adoption was universal.

Tell me about a time you addressed a significant operational or technology risk transparently.

What to coverThe posting emphasizes proactive risk management, operational problem-solving, transparency and accountability. Preparation suggestion: Explain the situation, how you communicated and acted with relevant partners, and what changed as a result.

Sharpen your knowledge

Topics to prepare

Firm-wide AI strategy and value
The posting calls for near-term impact and long-term transformation across investment research, client experiences, products and operations. Suggested revision exercise: compare two hypothetical AI initiatives on client value, implementation risk, expected return and time to impact.
Responsible AI and data governance
The role establishes standards for AI development and use, including ethics, data quality, privacy and compliance. Suggested revision exercise: outline the decisions a governance standard should cover from data selection through model use and ongoing review.
AI ethics and sensitive-data controls
The qualifications name bias detection, LIME, SHAP, anonymization, encryption and secure data enclaves as examples to understand. Suggested revision exercise: explain when each would help assess a model or protect client data, and where it would not be sufficient alone.
Architecture, cybersecurity and delivery
The posting requires understanding how software development, system architecture and cybersecurity work together to support scalable, secure AI systems. Suggested revision exercise: trace a hypothetical AI product from data intake to deployment, identifying architectural dependencies and security risks.
Executive governance and cross-functional influence
The Head of AI reports to the CEO, engages boards and works with investment, technology, finance, risk, compliance and client teams. Suggested revision exercise: map what each group would need to know before approving a hypothetical AI pilot.

Skills to revise

Build a phased AI roadmap
Practice drafting a one-page roadmap for hypothetical initiatives, separating pilots and near-term outcomes from longer-term capabilities. This rehearses the posting’s requirement to develop and execute a cohesive AI and technology strategy.
Present a board-level recommendation
Practice a brief verbal recommendation on a hypothetical AI investment covering value, cost, risks, controls and a decision request. This is relevant to the role’s regular board engagement and need to influence senior decisions with analysis.
Design an AI pilot assessment
Draft a pilot brief with a business objective, success measures, responsible teams, risk checks and a continue-or-stop decision. The posting calls for leading R&D and pilots, assessing applicability and return on investment, and delivering products.
Review a sensitive-data use case
Work through a hypothetical client-data AI use case: identify quality and privacy concerns, propose appropriate safeguards, and specify questions for data engineering, cybersecurity and compliance. This practices the posting’s data-governance and risk-management responsibilities.
Allocate resources and manage providers
Practice comparing two hypothetical AI-provider proposals against strategic fit, cost, security, delivery risk and expected value; then justify a budget allocation. This reflects the stated provider-relationship and AI and technology budget responsibilities.
Plan team development and adoption
Draft a short plan assigning ownership for a hypothetical AI initiative, identifying mentoring needs and addressing resistance from affected teams. This rehearses the explicit responsibilities to build a high-performing team and champion adoption across the firm.

Your preparation checklist

Map the stated qualifications to evidence
The posting requires a relevant master’s degree, 10+ years in AI strategy and transformation, and at least 5 years in leadership or management. Prepare a fact-checked evidence sheet with dates, roles and examples; leave any unsupported entry blank.
Draft a firm-wide AI strategy exercise
The role calls for near-term results and long-term transformation. Prepare a hypothetical roadmap for investment research, client experience and operations, showing priorities, measures of value and decision points. Label assumptions clearly.
Build an AI delivery walkthrough
The posting seeks AI R&D, product delivery and organization-wide adoption. Select a real initiative to explain from research or pilot through deployment, including your decisions, collaborators, measurable results and lessons learned.
Practice a responsible-AI design review
The role oversees AI standards, data quality, privacy and compliance. Prepare a hypothetical review covering bias checks, explainability where useful, sensitive-data controls, ownership and escalation; distinguish proposed controls from controls you have implemented.
Sketch a secure AI architecture
The posting expects fluency in software development, system architecture and cybersecurity. Draw a high-level AI system and explain data flows, access controls, encryption, monitoring, scalability and coordination with data engineering and cyber teams.
Document leadership and cross-functional influence
Prepare concise examples of building and mentoring a team and aligning investment, technology, finance, risk, compliance and client stakeholders. For each, identify the disagreement or obstacle, your action and the outcome.
Rehearse an executive investment decision
The role reports to the CEO, engages boards and manages budgets and provider relationships. Create a one-page hypothetical recommendation comparing an AI initiative’s cost, expected value, risks and vendor considerations; practice a brief board-level explanation.
Prepare questions about the mandate
Ask how the newly created department will set priorities, divide decisions with innovation and technology teams, measure AI adoption, and report risk and progress to executive and fund boards. These are questions to clarify, not assumptions about current processes.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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