Scrum Lead

Job description

The Emerging Tech Standard Delivery Team drives the adoption of advanced technologies and builds enterprise‑ready solutions for D&A Operations. We are seeking an experienced Scrum Master to support cross‑functional engineering teams delivering Data Science and Platform initiatives within LSEG’s Data & Analytics platform.  This role is critical to scaling Agile delivery, enabling collaboration across data, engineering, and platform teams, and driving predictable outcomes in a complex enterprise environment. The Scrum Master will lead multiple squads and projects using Scaled Agile practices, with strong delivery ownership and technical understanding.    Role, Responsibilities & Key Accountabilities:  • Own delivery orchestration for squads, ensuring work is planned, sequenced, and delivered against agreed milestones and success criteria   • Drive sprint level and release level planning, including backlog readiness, sprint cadence, milestones, and delivery checkpoints from onboarding through PPE, production, and BAU transition   • Facilitate Agile ceremonies including Sprint Planning, Grooming, Daily Stand ups, Reviews, and Retrospectives to ensure clarity, alignment, and steady delivery flow   • Lead delivery using Scaled Agile practices, managing cross team dependencies, risks, and impediments across multiple squads where applicable   • Coordinate execution across Data Science (Hub and Spoke), Engineering, QA, Operations, Product Owners, Architecture & Business stakeholders   • Track and manage delivery readiness, risks, and dependencies, including cross team, governance checkpoints, and regulatory or operational prerequisites   • Ensure adherence to governance processes, coordinating required artefacts, on- reviews, and sign offs & on-boarding  • Remove non-technical impediments and escalate issues that threaten delivery timelines or compliance, while preserving technical ownership with the Squad Technical Lead   • Drive end to end delivery focus, beyond individual sprints, including coordination through testing, UAT, go live, and transition to BAU   • Track and communicate delivery progress using Agile metrics, providing clear, concise status updates on progress, risks, and decisions to squads, leadership, and stakeholders   • Promote Agile best practices aligned to data science workflows, including experimentation, MLOps, and CI/CD integration   • Drive continuous improvement through actionable retrospectives, ensuring delivery transparency, predictability, and alignment with enterprise governance    Required Skills:  • Total 8+ Years of experience and 5+ years of relevant experience in Agile delivery, including Scrum Master roles in enterprise environments  • Own end-to-end delivery orchestration for the use case, ensuring alignment to milestones, timelines, and success criteria  • Drive sprint and release planning: backlog readiness, prioritization, sprint cadence, and delivery checkpoints  • Facilitate Agile ceremonies (stand-ups, sprint planning, reviews, retrospectives) to ensure clarity, alignment, and execution discipline  • Coordinate across Data Science, Engineering, Product, QA, and Operations to manage dependencies and remove blockers  • Track progress and provide clear status updates, enabling timely escalation and decision-making  • Ensure successful end-to-end delivery: testing, UAT, go-live, and smooth transition to BAU  • Apply Scrum/SAFe best practices alongside DevOps and data/platform delivery models  • Use tools such as Jira/Azure DevOps for tracking, reporting, and governance  • Demonstrate experience delivering Data Science, Analytics, or platform initiatives  • Act as a coach and facilitator to build high-performing teams and drive Agile maturity  • Influence stakeholders and teams without authority, ensuring collaboration and accountability  Preferred  • SAFe certification (SPC, RTE, or equivalent)  • Experience in financial services or other highly regulated environments  • Familiarity with alternative scaled Agile frameworks (LeSS, Disciplined Agile)  • Exposure to AI/ML pipelines, Data Scienc