Senior Data Scientist - Strategic ML & Forecasting

barclays; Glasgow; On-site; GBP 90,000 - 120,000; Full time

Job summary

Barclays is seeking a Data Scientist to extract insights from large data reserves and apply machine learning to drive strategic decisions and innovation. You will build predictive models, design complex analyses, and collaborate with senior stakeholders to realize commercial value.

The role emphasises production-grade Python development, scalable data pipelines, and advanced quantitative techniques across diverse data sources to inform risk and opportunity assessments.

Qualifications

• Experience in extracting insights from large data sets and presenting to non-technical stakeholders.
• Proficiency with statistical and ML methods including regression, clustering, NLP, and time-series analysis.
• Strong Python programming and production-grade software development skills.
• Experience with big data technologies and building data pipelines.

Responsibilities

• Identify, collect, and extract data from multiple sources.
• Clean, wrangle, and transform data for analysis and modeling.
• Develop and maintain efficient data pipelines for automated processing.
• Design and implement statistical and ML models to analyze patterns and trends.
• Develop predictive models to forecast outcomes and assess risks and opportunities.
• Collaborate with business stakeholders to translate data insights into value.

Skills

Strategic insights

Python

Machine learning

Statistics

Data wrangling

Communication

Big data technologies

Production-grade code

Tools

Pandas

NumPy

scikit-learn

TensorFlow

Job description

Barclays is seeking a Data Scientist to extract insights from large data reserves and apply machine learning to drive strategic decisions and innovation. You will build predictive models, design complex analyses, and collaborate with senior stakeholders to realize commercial value.

The role emphasises production-grade Python development, scalable data pipelines, and advanced quantitative techniques across diverse data sources to inform risk and opportunity assessments.