Director of Divisional Reporting and Predictive Analytics

The Director of Divisional Reporting and Predictive Analytics will partner with divisional stakeholders to build, maintain, and scale product-focused metrics and dashboards that improve operational transparency, decision-making, and performance management.

This role will own the predictive analytics technology roadmap and operating model for LSEG Operations, working closely with Engineering, Operations, and Product Management to design forward-looking predictive and generative analytics solutions.

The role will lead our divisional reporting teams supporting Risk Intelligence, FTSE and Markets as well as a small, highly skilled team across predictive analytics, machine learning, database development, automation, and front-end application engineering.

Key areas of focus for the role include:

• Divisional Engagement: Build trusted partnerships with divisional leadership teams, product managers, and embedded Operations and Engineering delivery teams.
• Data, Technology & Practices: Develop multiyear investment strategies, technology and data roadmaps, and ways of working that enable agile proof-of-concept testing and seamless transition of successful pilots to LSEG Engineering for build and deployment.
• Culture Building: Partner with LSEG leaders and Operations Academy to build the knowledge, awareness, and organizational capabilities required to enable innovation at scale.
• Leadership: Lead predictive analytics, automation, and data visualization specialists, ensuring deep expertise, rapid delivery, and measurable business outcomes.

Key Responsibilities:

• Continually assess the applicability of emerging technologies, models, and ways of working for LSEG Operations.
• Drive investment planning for the Operations Metrics Office’s multiyear predictive analytics, tooling, and data modelling capabilities.
• Lead the design and development of proof-of-concept capabilities powered by AI and machine learning models.
• Secure access to new data sources for LSEG Operations in anticipation of future organizational and customer requirements.
• Create an Operations culture of innovation by facilitating pilot programs that support strategic focus areas and remove perceived barriers to execution.
• Drive team development to improve efficiency, strengthen delivery capability, and expand Operations expertise.
• Sponsor Operations Academy communities of practice for AI/ML and other emerging technologies and ways of working.
• Collaborate with Engineering and Product Management to influence technology roadmaps and align delivery with operational priorities.
• Directly lead a team of AI, machine learning, automation, and visualization professionals, maintaining high standards for delivery, adoption of industry-leading practices, and realization of business outcomes.

Key Capabilities:

• Demonstrate deep subject matter expertise and the ability to influence organizational thinking.
• Conceptualize and address highly complex, enterprise-wide business challenges across LSEG.
• Develop and implement strategy for LSEG Operations, with clear linkage to measurable business outcomes.
• Understand and apply industry-leading practices for LSEG Operations, driving adoption where required to improve performance and resilience.
• Communicate highly technical concepts clearly to non-technical business and executive stakeholders.
• Develop creative insights and correlation studies that support executive planning, performance management, and management reporting.
• Foster and maintain collaborative working relationships with stakeholders across all levels of LSEG.

Observation of Internal Controls:

• Demonstrate adherence to internal controls by following relevant procedures, maintaining appropriate records, and implementing internal and external audit actions in a timely manner.

Candidate Profile / Key Skills:

• Strong interpersonal and written communication skills, with the ability to influence stakeholders across all organizational levels.
• Demonstrable experience leading AI and machine learning professionals.
• Strong understanding of Python programming concepts and leading practices, supported by solid SQL skills.
• Extensive knowledge of MLOps approaches and their application in scalable analytics delivery.
• Extensive experience applying cloud computing capabilities to data, analytics, or automation solutions.
• Entrepreneurial mindset with the ability to interpret complex problem statements, gather relevant data, and propose practical, creative solutions.
• Educated to degree level or able to demonstrate equivalent professional experience.