Moneyhub

Machine Learning Engineer

Moneyhub

full-time

Posted on:

Origin:  • 🇬🇧 United Kingdom

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Job Level

Mid-LevelSenior

Tech Stack

DockerJavaScriptNode.jsPython

About the role

  • As a Machine Learning Engineer at Moneyhub, you'll bridge the gap between data science and software engineering. You'll be responsible for developing production-ready data solutions that solve real user problems—focusing on delivering working code rather than just analysis or prototypes.
  • What You'll Work On:
  • - Data Enrichment Systems: Build and maintain systems that enhance raw financial data, including our transaction categorisation engine that underpins budgeting capabilities and affordability checking services
  • - Production-Ready Solutions: Transform data science concepts into robust, high-performance code that can handle our production workloads
  • - Pragmatic Algorithm Development: Create and optimize algorithms using the most appropriate techniques to solve specific user problems
  • - Data-Driven Product Innovation: Collaborate with product teams to translate business requirements into technical solutions that enrich financial data
  • - User Insights: Analyze user characteristics and segmentation to support business decisions and product development

Requirements

  • 3+ years of experience in data science or related engineering roles
  • Strong software engineering practices with proficiency in Python
  • Working knowledge of Node.js for backend integration
  • Experience working within a Start Up / Scale Up technology company
  • Experience building and deploying data solutions to production environments
  • Practical knowledge of data processing techniques and relevant frameworks
  • Understanding of when to apply ML algorithms vs. simpler approaches to solve problems
  • Experience with statistical analysis and ability to interpret results to drive decision-making
  • Proven ability to clean and prepare data for analysis and enrichment
  • Excellent communication skills with ability to present technical concepts to non-technical stakeholders
  • Bachelor's or Master's degree in a numerical or engineering subject (Data Science, Computer Science, Mathematics, or related field)
  • Beneficial: Experience with containerization using Docker
  • Beneficial: Has worked on high-performance data processing systems
  • Beneficial: Can perform data science analysis independently but focuses on production implementation
  • Beneficial: Understands the difference between exploratory work in notebooks and production-ready code
  • Beneficial: Experience optimizing algorithms for performance and scale
  • Beneficial: Demonstrates a pragmatic approach to problem-solving, always seeking the simplest solution that delivers the best results
  • Beneficial: Can evaluate when machine learning is appropriate and when simpler approaches would be more effective