Data Scientist CTOO

Job description

ABOUT US:LSEG (London Stock Exchange Group) is more than a diversified global financial markets infrastructure and data business. We are dedicated, open-access partners with a dedication to excellence in delivering the services our customers expect from us. With extensive experience, deep knowledge and worldwide presence across financial markets, we enable businesses and economies around the world to fund innovation, manage risk and create jobs. It’s how we’ve contributed to supporting the financial stability and growth of communities and economies globally for more than 300 years. Through a comprehensive suite of trusted financial market infrastructure services – and our open-access model – we provide the flexibility, stability and trust that enable our customers to pursue their ambitions with confidence and clarity. LSEG is headquartered in the United Kingdom, with significant operations in 70 countries across EMEA, North America, Latin America and Asia Pacific. We employ 25,000 people globally, more than half located in Asia Pacific. LSEG’s ticker symbol is LSEG. OUR PEOPLE:People are at the heart of what we do and drive the success of our business. Our culture of connecting, creating opportunity and delivering excellence shape how we think, how we do things and how we help our people fulfil their potential. We embrace diversity and actively seek to attract individuals with unique backgrounds and perspectives. We break down barriers and encourage teamwork, enabling innovation and rapid development of solutions that make a difference. Our workplace generates an enriching and rewarding experience for our people and customers alike. Our vision is to build an inclusive culture in which everyone feels encouraged to fulfil their potential. We know that real personal growth cannot be achieved by simply climbing a career ladder – which is why we encourage and enable a wealth of avenues and interesting opportunities for everyone to broaden and deepen their skills and expertise. As a global organisation spanning 70 countries and one rooted in a culture of growth, opportunity, diversity and innovation, LSEG is a place where everyone can grow, develop and fulfil your potential with meaningful careers. ROLE SUMMARY:Are you an experienced data scientist with a passion for building practical analytical solutions and turning complex data into meaningful insight? Do you want to help shape workforce strategy in a rapidly changing environment, exploring how AI, changing demand and new ways of working affect the capabilities and capacity Engineering needs? If so, we’d love to talk to you! ROLE OVERVIEW:As an individual contributor Lead Data Scientist within Workforce Strategy (WFS), you will take ownership of analytical projects that help Engineering understand and respond to its changing workforce needs. Working with WFS leadership and cross-functional partners, you will turn agreed business questions into practical analytical solutions, scenario models and evidence-based insights that inform workforce decisions. Your analysis will cover workforce size and shape, skills and capabilities, location and sourcing, and the implications of AI for work, productivity and capacity. We are looking for an applied data scientist who can turn evolving business questions into focused analysis, connect complex datasets, test hypotheses and develop reusable tools, models and visualisations. The emphasis is on practical problem-solving and decision support, rather than building and operating production AI systems. The successful candidate will combine strong technical judgement with curiosity, adaptability and a collaborative approach, working with Engineering, Finance, HR and Procurement to explain findings, make uncertainty visible and inform workforce choices. WHAT YOU'LL BE DOING: This hands-on role combines independent delivery of agreed analytical projects with interpretation of their workforce implications, helping WFS respond to changing priorities and move beyond reporting what has happened to understanding what it means and what choices should be considered next. Translate workforce questions into analytical approaches and build reusable solutions, including exploratory notebooks, scenario models and visualisations, to investigate capacity, role mix, skills, location, sourcing and cost. Connect and analyse workforce, financial, planning and delivery data to establish baselines, identify trends and dependencies, and test hypotheses. Develop scenarios and sensitivity analysis that make assumptions, trade-offs and uncertainty explicit. Investigate how AI and automation are changing activities, skills and capacity across developer and non-developer work. Distinguish adoption from measurable changes in output, speed, quality or capacity, without assuming that usage demonstrates productivity or workforce impact. Apply and help improve consistent analytical methods, definitions and validation, working with data owners to resolve differences and explain limitations. Understand source structures and data flows, document lineage, reconcile differences and make data gaps, limitations and alternative explanations visible. Work with WFS leadership and business and technical partners to agree analytical priorities based on decision value and evidence availability, then take ownership of delivering the analysis. Iterate solutions as needs change and communicate clear findings, strategic implications and options to senior stakeholders. WHAT YOU'LL BRING: Essential Skills: Bachelor’s degree in Engineering, Computer Science, Data Science, Statistics, Mathematics, Physics or related field, or equivalent work experience Demonstrable experience independently delivering data science projects that address complex business questions, building practical analytical solutions and translating findings into useful insight and decision support. Strong exploratory data analysis and statistical skills, including hypothesis testing, segmentation, trend analysis and scenario modelling; sound judgement in interpreting uncertainty, distinguishing correlation from causation and selecting methods appropriate to the evidence. Strong Python and SQL skills for preparing, connecting and analysing complex datasets, and developing statistical models, scenarios and clear visualisations. Ability to make analysis repeatable and adaptable as business questions change. Ability to work across complex enterprise datasets, understand how data is structured and flows between systems, and address inconsistent definitions, classifications and reporting periods without obscuring underlying differences. A rigorous approach to analytical quality, including checking data and results, documenting methods and assumptions, and ensuring findings can be reproduced and explained, with appropriate handling of confidential workforce data. Excellent communication and stakeholder skills, with the ability to frame business questions, explain complex findings to non-technical audiences and manage analytical priorities in a rapidly changing environment. Desirable skills: Experience in workforce analytics, strategic workforce planning or organisational analysis. Familiarity with skills, demand and capacity modelling, and assessing AI-related changes in productivity. Familiarity with business intelligence tools and enterprise data platforms, and experience maintaining shared analytical work, tracking changes and checking the accuracy and consistency of analysis. WHAT YOU'LL GET IN RETURN: High-impact projects: Build analytical solutions and insights that help Engineering understand its workforce, explore future scenarios and make informed choices as technology and business needs evolve. Competitive benefits: You will enjoy a strong compensation package, comprehensive benefits, and ongoing investment in career growth and skill development. Contribute to an evolving capability: Help improve WFS’s analytical tools, methods and evi