Business Process Analyst – Automation & Discovery
Digitalisation – DSM (Service Delivery), LSEG

About the Role

We are seeking a Business Process Analyst to join the Digitalisation team within DSM (Service Delivery) at LSEG. This role sits at the heart of process discovery, automation opportunity exploration, and digital transformation, helping shape well‑defined, data‑driven use cases that feed into our engineering workstreams.

You will analyse real‑world operational processes, triangulating between documentation, system data, process mining insights, and interviews with business teams. Your goal is to surface inefficiencies, uncover automation and AI enhancements, and translate them into clear, structured problem statements the engineering teams can act on.

This is a hands‑on, discovery‑focused role suited to someone who enjoys digging into how work really happens in practice, engaging closely with DSM Business Services, operational SMEs, Business Architects, and engineering leads in Data Engineering, Analytics, ML, and AI.

The role enables stronger clarity, alignment, and adoption of Digitalisation initiatives across DSM.

Key Responsibilities

Business Process Analysis

• Analyse end‑to‑end operational workflows, including manual steps, handoffs, approvals, exceptions, and rework loops.
• Combine process artefacts, interviews, and direct operational insight to understand how work is performed in practice, not just how it is documented.
• Engage with SMEs to uncover bottlenecks, challenges, and workarounds not easily visible in system logs.
• Detect inefficiencies, recurring manual effort, and areas where digital improvements could have real impact.

Data‑Driven Discovery & Insight

• Use SQL to explore operational datasets and quantify volumes, patterns, exceptions, and constraints.
• Use Python (e.g., Pandas) for lightweight analytical exploration to support hypothesis testing and prioritisation.
• Create simple Power BI visualisations to support discovery discussions.
• Where available, interpret process mining outputs (e.g., Celonis, QPR, Power Automate Process Mining) to validate real‑world execution paths and uncover hidden variants.
• Combine qualitative process understanding with quantitative insight to strengthen use‑case discovery.

Automation & Use‑Case Discovery

• Spot areas for workflow and rules‑based automation, analytics enhancements, data‑driven decision support, or ML/AI augmentation (in collaboration with engineering).
• Assess each potential enhancement using structured criteria including impact, feasibility, suitability, dependencies, and constraints.
• Translate discoveries into delivery‑ready use cases, including:
  • Clear problem statements
  • Scope and assumptions
  • Expected benefits
  • Risks and constraints
  • Success metrics and value logic

As‑Is → To‑Be Translation

• Create clear as‑is process maps and to‑be digital/automation concepts (BPMN or equivalent).
• Clearly articulate what will change, what will remain, and where automation or AI will be applied.
• Provide contextualised, well‑scoped inputs to engineering teams to support effective design and delivery.

Prioritisation Support

• Contribute input to a prioritised pipeline of initiatives based on business impact, complexity, dependencies, and readiness.
• Use simple prioritisation methods (e.g., impact vs effort) to support structured decision‑making.
• Help determine the most appropriate engineering workstream for each use case.

Operating Model Communication

• Produce clear, concise visualisations of processes, operating models, and future‑state workflows.
• Prepare communication materials and presentations that explain use cases, benefits, and expected changes in accessible, non‑technical language.
• Support alignment sessions by helping teams understand how processes and roles may evolve.

Adoption & Value Realisation Support

• Identify potential change impacts early in the lifecycle to support smoother solution adoption.
• Act as a feedback channel between business users and engineering teams during early rollout.
• Track intended vs realised outcomes and capture insights and lessons learned.
• Contribute to internal narratives articulating the value delivered by Digitalisation.

Collaboration & Continuous Improvement

• Work closely with DSM Business Services, operational SMEs, Business Architects, and engineering teams.
• Maintain a structured view of analysed processes and discovered enhancements.
• Contribute to improving templates, discovery approaches, prioritisation methods, and ways of working.

How This Role Fits into Delivery

This role operates upstream of engineering, strengthening discovery, clarity, and alignment. By providing high‑quality, well‑scoped problem definitions and validated opportunities, the role helps ensure Digitalisation initiatives are:

• Easier to deliver
• Better understood
• Faster to adopt
• Aligned to real business needs

The role does not manage delivery but provides critical inputs into it.

What Success Looks Like

• Processes are understood at execution level, not just theoretical or documented level.
• Opportunities entering engineering are clear, prioritised, and well‑defined.
• Discovery is grounded in both data and lived operational reality.
• Business teams understand what is changing, why, and how it benefits them.
• Digitalisation initiatives deliver measurable, traceable business impact.

Required Skills & Experience

• Experience in business process analysis, operational analysis, or continuous improvement roles.
• Ability to map and clearly explain processes (BPMN, flow diagrams, or equivalent).
• Working knowledge of SQL for operational data exploration.
• Basic–intermediate Python (Pandas) for analysis.
• Confidence engaging with SMEs to understand real workflows and recurring issues.
• Ability to convert qualitative and quantitative insights into structured use‑case documentation.
• Ability to create clear presentation materials for business and non‑technical groups.
• Experience in Agile or iterative environments.

Desirable Skills

• Exposure to process mining concepts (event logs, variants, throughput, conformance).
• Experience working with or interpreting outputs from tools such as Celonis, QPR ProcessAnalyser, ProM or Power Automate Process Mining (nice‑to‑have, not essential).
• Experience with Power BI or similar tools for exploratory analysis and insight communication.
• Experience or interest in business process optimisation or Lean/CI approaches.
• Exposure to automation, analytics, machine learning, or AI initiatives.
• Ability to interpret operational metrics to support prioritisation.
• Experience in regulated or operationally complex environments.
• Strong interest in digital transformation and intelligent automation.

Why Join Us

You will be part of a hands‑on Digitalisation function delivering practical, high‑impact transformation across DSM — and we’re excited about what you’ll bring to the team!
This is a great opportunity to grow your skills and make a visible difference from day one!

This role offers:

• Direct involvement in automation, data, ML, and AI initiatives
• Deep exposure to operational processes across Capital Markets
• Close collaboration with engineering teams
• Opportunities to grow analytical, process, discovery, and engagement skills
• The ability to visibly shape operational efficiency, resilience, and effectiveness