Head of Analytics Engineering

London Stock Exchange Group•New York City, NY

1d•Onsite

About The Position

LSEG is seeking a Head of Analytics Engineering to lead the strategy, design, and delivery of Analytics products and capabilities underpinning our Cross Asset, Fund, Private Markets and Predictive Analytics offerings, reporting to the Data and Analytics Engineering CIO. This role owns the end-to-end engineering of analytics capabilities across these offerings—including pricing, risk, derived metrics, and predictive (AI/ML-driven) analytics—ensuring they are scalable, production-grade, and aligned to client workflows. This role sits at the intersection of financial domain expertise, large-scale distributed systems engineering, and applied AI/ML, with accountability for how analytics products are engineered, productionised, and scaled across LSEG. AI/ML underpins predictive analytics capabilities and is increasingly embedded across Cross Asset, Fund, and Private Markets offerings; this role is accountable for establishing the engineering frameworks required for model integration, deployment, monitoring, and lifecycle management at scale. The role combines product-centric thinking with deep technical leadership, ensuring analytics capabilities are commercially differentiated, technically robust, and aligned to client needs. You will lead a high-performing organisation, shape engineering direction, and partner closely with Product, Research, and Commercial leadership to deliver impactful outcomes.

Requirements

Master's degree in computer science or computer engineering with deep expertise in scaling analytics computations for financial markets.
Strong domain knowledge in fixed income and derivatives, including pricing and risk concepts.
Strong programming expertise (Python, Java, C++) with proven experience building distributed data and analytics systems, including high-throughput pipelines and low-latency computation.
Strong understanding of AI/ML systems and production deployment, including model lifecycle management.
Experience within a financial data provider, exchange, sell-side institution, or quantitative firm with a comparable data and analytics ecosystems.

Responsibilities

Define and execute the Analytics Engineering strategy for a portfolio of analytics products, aligned to the Data and Analytics Product Engineering business unit.
Translate customer and market needs into scalable analytics capabilities, owning and evolving a forward-looking roadmap across analytics computations and AI/ML in partnership with Product and Commercial teams.
Contribute to LSEG’s engineering direction and market positioning as a recognised leader in analytics and data-driven capabilities.
Lead the architecture, design and delivery of production-grade analytics products (e.g., pricing and risk) – across real-time and batch use cases.
Set and enforce engineering best practices across code quality, testing, and observability.
Own delivery of complex, high-impact programmes, ensuring alignment across teams.
Provide strategic technical leadership, guiding architecture and critical design decisions.
Drive integration of AI/ML into analytics products, ensuring effective path to production, monitoring, and governance.
Evaluate and adopt emerging technologies to enhance analytics capabilities and commercial value.
Apply deep expertise in fixed income and derivatives to shape analytics products and ensure analytics outputs (e.g., pricing, risk and derived metrics) are accurate, performant, and aligned to client workflows.
Operate with an enterprise mindset, partnering closely with Sales, Product, Research, Data Platforms, Communities and adjacent Engineering teams to deliver outcomes that scale beyond a single domain.
Build trusted, high-impact relationships across LSEG, aligning Analytics Engineering priorities to groupwide strategy and customer outcomes.
Influence without authority, bringing teams together around common goals, resolving tradeoffs, and driving decisions that optimise for LSEG.
Translate complex technical concepts into clear business insights and decisions.
Lead and scale high-performing analytics engineering organization, fostering an inclusive and collaborative culture.
Ensure alignment and collaboration across teams, programmes, and senior stakeholders.