Head of Data Science & AI

Janus Henderson Investors•Denver, CO

1d•Hybrid

About The Position

The Head of Data Science & AI spearheads the asset management firm’s 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 usage. In 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.

Requirements

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 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).
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).

Nice To Haves

Asset Management Analytics: Direct experience within an asset management firm’s 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 firm’s innovation strategy.
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 today’s 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 firm’s 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.

Responsibilities

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.
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.
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.
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).
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.

Benefits

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
Annual discretionary bonus award from the profit pool
Competitive compensation
Pension/retirement plans
Various health, wellbeing and lifestyle benefits