Data Scientist

RED Global; Sheffield; Hybrid; GBP 60,000 - 90,000; Full time

Job summary

RED Global is seeking an experienced Data Scientist to optimise fraud models and ML infrastructure. The role involves hands-on ML model tuning, GCP resource management, and delivering practical recommendations for cost reduction and performance gains.

You will work across production ML pipelines and collaborate with engineering teams to drive end-to-end enhancements. The ideal candidate has strong Python and SQL skills, experience with Spark, and a track record of scalable ML deployments in

Qualifications

• Experienced Data Scientist or ML Engineer.
• Strong ML model optimisation and deployment skills.
• Proven ability to design end-to-end ML pipelines.

Responsibilities

• Assess and optimise fraud and ML models to improve performance and reduce costs.
• Provide implementation-ready recommendations with effort estimates and impact.
• Guide engineering and data science teams at configuration and code level.
• Analyse and optimise GCP compute, storage and networking for cost and performance.
• Implement monitoring and observability for ML workloads.

Skills

Machine Learning

Python

Data engineering

SQL

Tools

Google Cloud Platform (GCP)

Spark

CI/CD

Job description

We are looking for an experienced Data Scientist with strong Machine Learning, Google Cloud Platform (GCP), Python and data engineering expertise to support the optimisation of fraud models and ML infrastructure.

Contract information

Job title: Data Scientist

Location: 3 days in Sheffield and 2 days remote

Contract type: Inside IR35

Key Responsibilities

• Assess and optimise existing fraud and machine learning models, identifying opportunities to improve performance and reduce compute and inference costs.
• Produce practical, implementation-ready recommendations, including estimated effort, cost savings and technical impact.
• Provide both configuration-level and code-level guidance to engineering and data science teams.
• Analyse and optimise GCP compute, storage and networking, balancing cost, performance and reliability.
• Implement and improve monitoring and observability across ML workloads, including cost, performance and reliability metrics.
• Support the design, validation and deployment of fraud and machine learning models end-to-end.
• Strong commercial experience as a Data Scientist, ML Engineer or similarly technical Machine Learning specialist.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Experience optimising cloud-based workloads for cost, performance and reliability.
• Strong SQL.
• Experience building, supporting or optimising production Machine Learning models.
• Experience with end-to-end ML and data pipelines.
• Strong understanding of model training, inference and feature engineering.
• Experience with Spark or similar distributed data processing technologies.
• Experience implementing engineering best practices including CI/CD and automated testing.
• Ability to analyse an existing technical environment and provide clear, actionable and commercially measurable recommendations.