Head of Data Science & AI

Key Responsibilities

• AI Strategy: Define and lead a comprehensive AI strategy for Janus Henderson. Develop a vision for how data science, machine learning, and AI can support key business objectives—from investment decision support and risk management to client analytics and efficiency improvements. Continuously refine this strategy based on emerging technologies and business needs.
• Model Development & AI Innovation: Lead a team of data scientists and AI engineers in developing predictive models and AI solutions. Identify high-impact use cases for AI at Janus Henderson—such as quantitative models for investment research, algorithms for detecting compliance anomalies, predictive analytics for client behavior, or process automation with AI. Guide the team through the model development life cycle (from POC to deployment), ensuring models are delivering business value and are well‑maintained.
• AI Governance & Ethics: Establish an AI governance framework to ensure the responsible use of AI. Set standards for model validation, transparency, and fairness, and ensure compliance with emerging AI regulations. Provide oversight so that AI‑driven decisions align with fiduciary responsibilities and ethical standards (e.g., avoiding biased outcomes or misuse of data). This role drives the key mandate—implementing guidelines, model risk management processes, and auditability for AI systems.
• Enablement & Collaboration: Encourage collaboration between the Data Science team and other units (IT, Investment, Client Groups, Risk & Compliance, Operations, etc.), acting as a bridge to integrate AI solutions into business processes. Work closely with the Value Stream Technology Heads to embed analytics in their domains (e.g., integrating AI models into a CRM or using analytics to improve internal processes).
• Emerging Technology & Thought Leadership: Keep the firm at the forefront of technological advances in AI and data science. Monitor industry trends (like new AI techniques, fintech innovations, or regulatory changes around AI) and evaluate their applicability to the firm. Lead innovation initiatives or pilot programmes and assess ROI. Advocate for investments in data science capabilities that yield competitive advantage.

Required Qualifications

• Education: Master’s or Ph.D. in Computer Science, Data Science, Statistics, Engineering, or related quantitative field. Strong academic grounding in machine learning, statistics, and algorithms is expected.
• Experience: 10+ years in data science, analytics, or related technology roles, with at least 5 years in a leadership or managerial capacity. A background that includes work in financial services, asset management, or capital markets is highly desirable.
• Technical Proficiency: Deep expertise in machine learning techniques, statistical modeling, and data analysis. Hands‑on experience developing and deploying models (e.g., predictive models, NLP, time‑series forecasting) and working with large datasets. Familiarity with model risk management practices in a regulated industry.
• Industry Knowledge: Solid understanding of the asset management business—investment products, portfolio management processes, performance analytics, and client servicing. Awareness of how data and AI are used in investment management (e.g., alpha generation, risk analytics, regulatory compliance reporting).
• Leadership & Communication: Demonstrated ability to lead teams of data scientists/analysts and to manage complex projects or programmes. Excellent communication skills, especially in distilling complex analytical findings into business insights. Experience presenting to senior executives or committees and advocating for data initiatives in business terms.

Preferred Experience

• Asset Management Analytics: Direct experience within an asset management firm’s analytics or quant research team. Participation in projects such as building investment signal models, automating risk analytics, or creating client personalization algorithms is a strong advantage.
• AI Governance Implementation: Experience establishing governance processes for AI/ML—forming model review committees, setting up monitoring frameworks, and ensuring compliance with regulations (e.g., EU AI Act or SEC guidance on model risk). Familiarity with ethical AI frameworks and industry best practices.
• Advanced Analytics Tools: Hands‑on familiarity with quantitative finance libraries, time‑series databases, or visualization tools common in finance (e.g., Matplotlib/Plotly, Tableau). Experience using AI in specialised contexts (like alternative data for investments or AI in algorithmic trading) can be valuable.
• Innovation & Research: Published work, patents, or conference presentations in AI or data science, particularly relating to finance. While not required, such experience demonstrates thought leadership and commitment to staying at the cutting edge.

Technical Skills

• Programming & ML Tools: Proficiency in Python (pandas, scikit‑learn, TensorFlow/PyTorch), R, and SQL. Experience with notebooks (Jupyter) and collaborative platforms. Familiarity with Git and code‑base collaboration.
• Machine Learning & AI: Strong grasp of machine learning algorithms and their appropriate usage. Experience building, tuning, and deploying models in production. Knowledge of MLOps practices—model versioning, automated testing, continuous integration/deployment.
• Data Platforms: Experience with relational databases, NoSQL or time‑series databases, and big data frameworks. Skilled in cloud‑based data services (AWS Redshift, Azure Synapse, Google BigQuery) and distributed computing when needed.
• Analytics & BI: Ability to use data visualisation and BI tools (Tableau, Power BI, Python libraries) to create compelling data stories for non‑technical audiences.
• AI Ethics & Security: Familiar with bias detection, explainability techniques (LIME, SHAP), data anonymisation, and secure handling of client data.
• Strategic Vision & Innovation: Ability to formulate a clear vision of how AI and analytics can drive business value and communicate that vision compellingly.
• Ethical Leadership: High ethical standards for data usage and AI. Advocates responsible AI and can say “no” to risky use cases.
• Communication & Storytelling: Ability to explain complex analytical concepts in plain language. Produces clear documentation and guidelines.
• Collaboration & Influence: Works effectively across organisational boundaries, partners with IT, investment, compliance, and client teams, and influences senior‑level decision‑making.
• Mentorship & Talent Development: Builds a strong data science team, mentors junior scientists, and promotes continuous learning.
• Problem‑Solving & Resilience: Creative problem‑solving, methodical approach to challenges, and persistence in the face of setbacks.

What to Expect When You Join Our Firm

• Hybrid working and reasonable accommodations
• Paid volunteer time to step away from your desk and into the community
• Support to grow through professional development courses, tuition/qualification reimbursement and more
• All‑inclusive approach to Diversity, Equity and Inclusion
• Maternal/paternal leave benefits and family services
• Complimentary subscription to Headspace—the mindfulness app
• Corporate membership to ClassPass and other health and well‑being benefits
• Unique employee events and programmes including a 14er challenge
• Complimentary beverages, snacks and all employee happy hours

Supervisory Responsibilities

• Yes

Potential for Growth

• Regular training
• Continuing education courses

Annual Bonus Opportunity: Position may be eligible to receive an annual discretionary bonus award from the profit pool. The profit pool is funded based on company profits. Individual bonuses are determined based on company, department, team and individual performance.

Benefits: Janus Henderson is committed to offering a comprehensive total rewards package to eligible employees that includes competitive compensation, pension/retirement plans, and various health, wellbeing and lifestyle benefits. To learn more about our offerings please visit the Why Join Us section on the careers page.

Janus Henderson Investors is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status. All applications are subject to background checks.

You should be willing to adhere to the provisions of our Investment Advisory Code of Ethics related to personal securities activities and other disclosure and certification requirements, including past political contributions and political activities. Applicants’ past political contributions or activity may impact applicants’ eligibility for this position.

You will be expected to understand the regulatory obligations of the firm, and abide by the regulated entity requirements and JHI policies applicable for your role.

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