Head of Data Science & AI

Why work for us

A career at Janus Henderson is more than a job its about investingin a brighter future together.

Our Mission at Janus Henderson is to help clients define and achieve superior financial outcomes through differentiated insights disciplined investments and world-class service. We will do this by protecting and growing our core business amplifying our strengths and diversifying where we have the right.

Our Values are key to driving our success and are at the heart of everything we do:

Clients Come First - Always Execution Supersedes Intention Together We Win Diversity Improves Results Truth Builds Trust

If our mission values and purpose align with your own we would love to hear from you!

Your opportunity

The Head of Data Science & AI spearheads the asset management firms data-driven initiatives responsible for developing and executing a strategy to harness data and artificial intelligence across the organization. This role oversees advanced analytics AI model development and the governance of AI/ML an industry where data is a critical asset the Head ensures that the firm leverages AI ethically and effectively to gain insights--improving investment research enhancing client experiences and optimizing operations. They establish frameworks for AI innovation governance and collaboration recognizing that instilling ethical AI frameworks and a strong leadership vision is critical in investment management. This individual combines deep technical expertise in AI and data science with leadership and industry knowledge to transform data into a competitive advantage while upholding the strict standards of accuracy transparency and client trust expected in asset management..

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. This includes setting standards for model validation transparency and fairness and ensuring 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). Senior leadership has set a strategic vision and ethical culture for AI development and this role is the key driver of that 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. This includes working closely with the Value Stream Technology Heads to embed analytics in their domains (for instance 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 programs and assess ROI. Advocate for investments in data science capabilities where they can yield competitive advantage as proactive AI governance and innovation can provide significant advantages and risk reduction.

Required Qualifications

• Education: Masters 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 of experience 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 due to the domain-specific nature of data in this industry.
• 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--including investment products (equities fixed income etc.) portfolio management processes performance analytics and client servicing. Awareness of how data and AI are used in investment management (for example understanding concepts like alpha generation risk analytics and regulatory compliance reporting).
• Leadership & Communication: Demonstrated ability to lead teams of data scientists/analysts and to manage complex projects or programs. Excellent communication skills especially in distilling complex analytical findings into business insights. Experience presenting to senior executives or committees and ability to advocate for data initiatives in business terms (e.g. explaining the value of a model in improving returns or efficiency).

Preferred Experience

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

Technical Skills

• Programming & ML Tools: Proficiency in programming languages such as Python (with libraries like pandas scikit-learn TensorFlow/PyTorch) and R. Comfortable with SQL for data querying. Experience with notebooks (Jupyter etc.) and collaborative platforms for data science. Familiarity with version control (Git) and collaboration in a code environment.
• Machine Learning & AI: Strong grasp of machine learning algorithms (regression classification clustering tree-based models neural networks etc.) and their appropriate usage. Experience with building tuning and deploying models in production. Knowledge of MLOps practices--using tools for model versioning automated testing and continuous integration/deployment of ML models.
• Data Platforms: Experience with databases and data processing: relational databases (SQL Server Oracle etc.) NoSQL or time-series databases and big data frameworks. Skilled in using cloud-based data services (e.g. AWS Redshift Azure Synapse Google BigQuery) and distributed computing when needed for large datasets.
• Analytics & BI: Ability to work with data visualization and BI tools to create compelling data stories. Knowledge of tools like Tableau Power BI or Python visualization libraries to present insights to non-technical audiences. Understanding of statistical analysis and proficiency in relevant software (e.g. using R or Python for statistical tests or Excel for quick analysis) is expected.
• AI Ethics & Security: Familiar with techniques and tools to ensure AI ethics and data security--for example bias detection in models explainability techniques (LIME SHAP values) and data anonymization methods. Understanding of how to implement controls so that sensitive data (like client data) is handled properly in model development (e.g. encryption secure data enclaves).

Soft Skills & Leadership Competencies

• Strategic Vision & Innovation: Ability to formulate a clear vision of how AI and analytics can drive business value and to communicate that vision compellingly. A forward-looking mindset that not only solves todays problems but also anticipates future opportunities and challenges in the data/AI space.
• Ethical Leadership: High ethical standards with regard to data usage and AI. Proactively advocates for responsible AI ensuring models are used in the best interest of clients and the firms fiduciary duty. Willingness to say no to use cases that pose undue ethical or compliance risks and to set a tone of doing AI the right way (consistent with emerging best practices and regulations).
• Communication & Storytelling: Exceptional ability to explain complex analytical concepts in plain language. Can tell the story behind data to senior executives or portfolio managers who may not be experts in AI thus driving buy-in. Also capable of writing clear documentation and guidelines (for governance for example) that can be understood across the firm.
• Collaboration & Influence: Works effectively across organizational boundaries. Partners with IT for infrastructure with investment teams for domain knowledge with compliance for controls and with client teams for insights. Able to influence decision-making at the senior level by backing recommendations with solid data and analysis.
• Mentorship & Talent Development: Committed to building a strong data science team. Mentors junior data scientists promotes continuous learning (like encouraging keeping up with AI research or obtaining professional certifications) and attracts top talent by fostering an environment of intellectual curiosity and impact.
• Problem-Solving & Resilience: A creative problem-solver who can approach challenges (like data quality issues model performance problems or resource constraints) methodically and persistently. Displays resilience in the face of setbacks--for example if a model fails to produce expected results iteratively improves it or pivots approach. Maintains focus on delivering tangible results (insights efficiencies predictive accuracy) that align with business goals thereby reinforcing the value of data science within the organization.

What to expect when you join our firm

• Hybrid working and reasonable accommodations
• Generous Holiday policies
• 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 programs including a 14er challenge
• Complimentary beverages snacks and all employee Happy Hours

Supervisory responsibilities

• Yes

Potential for growth

• Mentoring
• Leadership development programs
• Regular training
• Career development services
• Continuing education courses

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.

At Janus Henderson Investors were committed to an inclusive and supportive environment. We believe diversity improves results and we welcome applications from all backgrounds. Dont worry if you dont think you tick every box we still want to hear from you! We understand everyone has different commitments and while we cant accommodate every flexible working request were happy to be asked about work flexibility and our hybrid working environment. If you need any reasonable accommodations during our recruitment process please get in touch and let us know at

#LI-LN2 #LI-HYBRID

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 career pagehere.

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.

Janus Henderson (including its subsidiaries) will not maintain existing or sponsor new industry registrations or licenses where not supported by an employees job functions (as determined by Janus Henderson at its sole discretion).

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.