LSEG is hiring for an fresher entry level Data Scientist role in India

Position:

• Data Scientist

Company:

• London Stock Exchange Group (LSEG)

Location:

• Bangalore, India (offices at RMZ Infinity and Divyasree Technopolis)

Job type:

• Full-time

Job mode:

• Onsite

Job requisition id:

• R0106477

Years of experience:

• 0–3 years

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Company Description:

• LSEG (London Stock Exchange Group) is a globally recognized leader in financial market infrastructure and data solutions.

• The company plays a critical role in maintaining global financial stability by offering trusted platforms, infrastructure, and insights that power the financial world.

• With operations spanning 65 countries and a workforce of over 25,000 professionals, LSEG combines financial expertise with cutting-edge technology to deliver services across capital markets, data analytics, and post-trade solutions.

• LSEG’s values—Integrity, Partnership, Excellence, and Change—are central to its culture and drive all major decisions and interactions.

• The company focuses heavily on sustainable development, inclusive growth, and supporting the green economy.

• Its India operations are Great Place to Work certified for 2025–2026, reflecting its strong commitment to employee engagement, culture, and long-term well-being.

• With initiatives across AI, machine learning, and financial engineering, LSEG offers professionals a chance to work on impactful challenges while growing their careers in a collaborative and future-facing environment.

• Employees are encouraged to bring their authentic selves to work and contribute meaningfully to global-scale projects that make tangible economic and social impacts.

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Profile Overview:

• The position sits within LSEG’s Group Investment Management (GIM) team.

• GIM is a group-level operations function that manages capital planning, change management oversight, and project delivery assurance across LSEG’s high-stakes investment portfolio.

• The Data Scientist role is pivotal in providing predictive analytics and risk forecasting for enterprise-wide change programs.

• You will be responsible for building machine learning models to detect program execution risk and support high-level decisions by senior leadership.

• The insights you generate will enable LSEG to make timely interventions, avoid financial overruns, and optimize the success of strategic transformation initiatives.

• The role blends traditional data science skills with strategic influence, as your work will directly shape the way LSEG allocates financial and human capital.

• The role also contributes to developing the company’s program delivery standards and frameworks.

• You’ll be solving large-scale operational problems and converting abstract business challenges into quantifiable risk insights.

• The GIM team operates at the nerve center of the organization’s strategy and offers a career-defining opportunity to drive change through data.

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Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related field.

• Strong programming skills in Python or R, with solid experience in libraries like scikit-learn, TensorFlow, or equivalent tools.

• Proficiency in SQL and hands-on experience in data wrangling and building ETL pipelines.

• Solid understanding of statistics, probability, and hypothesis testing.

• Demonstrated experience in building machine learning models in a professional context, especially around risk prediction or forecasting.

• Ability to work with unstructured data and apply LLM prompt engineering techniques to extract meaningful features.

• Exposure to visualisation tools such as Tableau or matplotlib to translate findings for business users.

• Strong written and verbal communication skills, especially the ability to explain technical ideas to non-technical stakeholders.

• Familiarity with cloud platforms like AWS, particularly Bedrock and Snowflake, is a plus.

• Experience in enterprise software tools such as JIRA, Asana, or Clarity is desirable.

• Domain knowledge in financial services, program delivery, or ERP tools like SAP and Workday is beneficial.

• Passion for continuous learning, collaboration, and delivering meaningful business value.

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Additional Info:

• This role is based in LSEG’s Bangalore offices and operates in a fully onsite model.

• The Data Scientist will be contributing directly to mission-critical initiatives at the enterprise level.

• As part of the GIM team, you’ll work with multiple business stakeholders including program managers, governance leaders, and data platform teams.

• The culture is fast-paced but supportive, valuing curiosity, transparency, and impact.

• There’s a strong emphasis on ethical AI, transparent risk communication, and creating tools that foster informed decision-making.

• You’ll also have access to a wide range of resources for professional development, including access to training platforms and internal mentoring.

• The company offers competitive compensation along with comprehensive benefits including healthcare, retirement planning, and paid volunteering days.

• This role is a perfect fit for early-career professionals who are eager to work on business problems that span analytics, technology, and executive decision-making.

• The selected candidate will also participate in developing frameworks and standards that guide how change programs are structured across LSEG.

• It’s more than just a technical role—it’s a strategic opportunity to shape how global financial transformation initiatives are executed and monitored.

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Detailed Responsibilities and Role Expectations:

Machine Learning & Risk Modeling

• Design, train, and fine-tune models that assess risks in large-scale program execution.

• Build early-warning risk indicators based on past delivery performance and predictive modeling.

• Apply regression, classification, and clustering algorithms to identify hidden risks.

• Track model accuracy, recalibrate as necessary, and document assumptions transparently.

• Regularly experiment with new ML architectures that might improve risk signal quality.

Analytics, Dashboards, and Executive Reporting

• Collaborate with MI and dashboarding teams to integrate insights into executive dashboards.

• Build visualizations that are intuitive and suited for non-technical leadership.

• Ensure risk metrics are tracked, visible, and tied to actionable intervention thresholds.

• Automate the reporting pipeline to reduce manual overhead and improve accuracy.

• Recommend visual storytelling improvements to strengthen clarity and decision support.

Data Engineering and Infrastructure

• Clean, preprocess, and integrate data from diverse sources including internal databases, APIs, and enterprise software systems.

• Build robust data pipelines that feed ML models and dashboards.

• Work with data platform teams to ensure high data quality and pipeline resilience.

• Create scalable ETL workflows that can handle ever-expanding investment data.

• Establish automated validation rules to identify anomalies in source data streams.

Business Collaboration & Strategic Influence

• Meet regularly with investment planners, risk managers, and delivery leads to understand emerging needs.

• Translate ambiguous business risks into measurable indicators suitable for modeling.

• Advocate for a data-first mindset when planning strategic investments.

• Actively contribute to working sessions that define LSEG’s change delivery philosophy.

• Present findings to executives and influence resource reallocation decisions.

Governance & Delivery Frameworks

• Help define group-level standards for delivery assurance and risk reporting.

• Document model dependencies, key assumptions, and limitations for transparency.

• Provide input on delivery governance evolution, especially on how predictive analytics can be embedded.

• Contribute to templates, guidelines, and tooling used by delivery teams globally.

• Audit risk scoring mechanisms periodically and ensure alignment with strategic goals.

Technology Evaluation & Innovation

• Stay informed about emerging trends in ML/AI, especially for operational risk modeling.

• Evaluate new tools and techniques that could improve model performance or interpretability.

• Test open-source LLMs or fine-tuned models for extraction tasks on program documents.

• Promote experimentation within the team and lead short tech spikes or POCs.

• Track impact from previous innovation initiatives and refine priorities.

Support for Investment Planning

• Analyze multi-year investment scenarios using data models and simulations.

• Estimate ROI and risk probability for upcoming strategic proposals.

• Support the Group Investment Committee with clear and concise evidence for decision-making.

• Provide analytical support during budgeting, reprioritization, or mid-year reviews.

• Help optimize the capital allocation process using historical success rates and risk benchmarks.

Please click here to apply.