Data Scientist

An exciting opportunity is available for a Data Scientist to join a newly launched, high-performing team based in Newcastle City centre. Backed by leaders in global strategy and finance, this new venture is focused on solving complex data challenges that support investment decision-making at pace and scale.

The ideal candidate will be passionate about working with large, diverse datasets – particularly alternative and financial data – and enjoy translating analytical insights into actionable business outcomes.

Key Responsibilities

• Partner closely with investment professionals to support data-driven decision-making
• Design and implement predictive models using structured and unstructured data
• Collaborate with data engineers to optimise data pipelines for analytics use
• Build and enhance robust data products that address real business needs
• Stay current with emerging technologies and share best practices across the team

Requirements

• Proven experience in data science or related roles
• Proficiency in Python and SQL; familiarity with Azure and Databricks is beneficial
• Strong academic background with a 2:1 or higher in a quantitative discipline (e.g., Mathematics, Engineering, Computer Science, Economics)
• Knowledge of time-series and financial data, and experience working with alternative datasets
• Excellent communication skills and a structured, detail-oriented approach
• Comfortable working independently in a fast-paced, problem-solving environment

Benefits

• Based in a modern office in Newcastle City Centre
• Direct exposure to investment and market-related work
• Opportunity to contribute to high-impact projects and develop domain expertise
• A collaborative environment that encourages curiosity, innovation, and ownership
• Competitive compensation packag

Interview Process

• Technical Coding Challenge
• Aptitude Assessment
• Interviews with Technology Management
• Final Technical Interview with Team Members

This role is ideal for those seeking a stimulating data science position at the intersection of finance, analytics, and innovation—without the red tape of legacy structures