Lead Data Scientist

Workday

Data Science Lead at Barclays shaping AI and Data Science strategies while leading a high-performing team. Utilizing advanced analytics and machine learning to drive business outcomes.

Posted 7/27/2026full-timeLondon • 🇬🇧 United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in Advanced Python Development and Machine Learning, with a strong focus on building scalable AI solutions and leading data science projects. Proven ability to collaborate with stakeholders and translate complex data insights into actionable business strategies.

Highest-signal resume keywords

Advanced Python DevelopmentMachine Learning & AITechnical LeadershipGenerative AI & Large Language ModelsStakeholder Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalyticsData CleaningData WranglingPredictive ModelingStatistical AnalysisCausal InferenceTime Series ForecastingDeep LearningGraph AnalyticsOptimisation

Soft Skills

People LeadershipCoachingCollaborationInfluencing

Tools & Technologies

NumPyPandasScikit-learnPyTorchContinuous IntegrationContinuous DeploymentVersion Control

Industry Keywords

Enterprise AI SystemsData ScienceAutomated Data AcquisitionData PipelinesAI Solutions

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorchScikit-Learn

About the role

Key responsibilities & impact

Use innovative data analytics and machine learning techniques to extract valuable insights from the bank's data reserves
Identification, collection, extraction of data from various sources, including internal and external sources
Performing data cleaning, wrangling, and transformation to ensure its quality and suitability for analysis
Development and maintenance of efficient data pipelines for automated data acquisition and processing
Design and conduct statistical and machine learning models to analyse patterns, trends, and relationships in the data
Development and implementation of predictive models to forecast future outcomes and identify potential risks and opportunities
Collaborate with business stakeholders to seek out opportunities to add value from data through Data Science

Requirements

What you’ll need

Advanced Python Development – Expert knowledge of Python and modern data science libraries including NumPy, Pandas, scikit-learn and PyTorch.
Machine Learning & AI – Demonstrated experience designing, developing, deploying and monitoring production-grade machine learning solutions.
Software Engineering Best Practices – Experience building modular, maintainable solutions using testing frameworks, Continuous Integration/ Continuous Deployment pipelines, version control and deployment automation.
Technical Leadership – A proven ability of leading complex data science projects, defining technical standards and driving innovation across teams.
People Leadership & Coaching – Experience leading, mentoring and developing technical professionals, creating an environment where individuals and teams can thrive.
Enterprise AI Systems – Experience designing and implementing scalable, governed AI solutions within a large organisation.
Strategic Leadership – Ability to define long-term technical roadmaps, influence senior stakeholders and lead transformational change.
Generative AI & Large Language Models – Proven experience with Large Language Models, agentic AI solutions and modern AI architectures.
Advanced Data Science Techniques – Experience applying specialised methods such as causal inference, time series forecasting, optimisation, graph analytics or deep learning.
Stakeholder Management – Ability to translate complex technical concepts into clear business recommendations and influence decision-making at senior levels.

Benefits

Comp & perks

Flexible working arrangements
Professional development opportunities