Technical Product Lead
London Stock Exchange Group
In this role you will lead the product direction for LSEG’s Equity and Multi-Asset index offerings and an internal AI tool. You’ll bridge research, engineering, and client needs to ship impactful features, balancing speed and strategic value. The position sits in the Equities Product, Research & Design team and will grow into a broader product capability as the AI tool scales. You’ll operate in a fast-paced, ambiguous environment and drive outcomes through cross-functional collaboration and user-centered prioritisation.
Pay / Benefits- healthcare
- retirement planning
- paid volunteering days
- wellbeing initiatives
- career development
- flexible benefits
- Define product strategy and roadmap for Equity & Multi-Asset indices and the AI tool, aligned to global priorities
- Capture, categorize and prioritise user feedback from internal and external users to inform the feature pipeline
- Conduct market and competitive analysis to identify demand and growth opportunities and translate into index enhancements
- Make pragmatic prioritisation decisions balancing client needs, technical effort, and commercial value
- Collaborate with Sales and Marketing on positioning, GTM and revenue growth across institutional and wealth segments
- Act as a bridge between AI tool engineers and end users, providing progress updates to senior stakeholders
- 5+ years in product, technical product or research roles, preferably in financial services or index/data industries
- Technical fluency to work with software and AI/ML developers; understanding of AI product development and shipping
- Strong ability to synthesise feedback and defend trade-offs
- Experience supporting a software or data product, with familiarity in equity and/or multi-asset products
- Excellent analytical and communication skills across research, tech, sales, operations and marketing
- feedback-synthesis
- prioritisation instinct
- cross-functional collaboration
- AI/ML product knowledge
- Python coding familiarity
- data science workflows
Reference: WJ-747_30175051