G MASS

Senior Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeLondon, UKNo salary listed

About the Job

Requirements

A bachelor's or master's degree in Statistics, Data Science, Computer Science, or a related field is required.
Strong experience managing the full machine learning model lifecycle, including batch and online models, is required.
A solid understanding of statistical methods, including generalized linear models and modern machine learning techniques, is required.
The candidate must be able to build and deploy production-quality Python applications using pandas and scikit-learn.
Experience with DevOps and machine learning tooling, including Kubernetes, Docker, continuous integration and continuous delivery, and git-based workflows, is required.
Familiarity with cloud platforms such as Amazon Web Services and cloud data warehouses such as Snowflake and SQL is required.

Responsibilities

Build and support machine learning lifecycle tooling for model deployment, monitoring, and alerting.
Maintain and improve the Kubeflow environment for Data Scientists and Actuaries.
Create pricing analytics tools to accelerate impact analysis and reduce manual work.
Collaborate with pricing and product teams to deliver high-impact tooling.
Communicate complex concepts clearly to technical and non-technical audiences.

Senior Machine Learning Engineer

G MASS · London, United Kingdom (Hybrid) · Contract · Posted 2026-01-06

Workplace: hybrid

Description

G MASS Consulting are supporting a leading Audit and Advisory business. We’re looking for a Machine Learning Engineer to shape and scale their pricing technology. In this role, you’ll design and own ML platforms that streamline pricing workflows, support rapid model deployment, and ensure models perform reliably at scale. Partnering with Data Science, Actuarial, and Product teams.

Responsibilities:

Build and support ML lifecycle tooling for model deployment, monitoring, and alerting
Maintain and improve the Kubeflow environment for Data Scientists and Actuaries
Create pricing analytics tools to accelerate impact analysis and reduce manual work
Collaborate with pricing and product teams to deliver high-impact tooling
Communicate complex concepts clearly to technical and non-technical audiences

Requirements

Bachelor’s or Master’s degree in Statistics, Data Science, Computer Science, or a related field
Strong experience managing the full ML model lifecycle (batch and online)
Solid understanding of statistical methods, including GLMs and modern ML techniques
Proven ability to build and deploy production-quality Python applications (pandas, scikit-learn)
Experience with DevOps and ML tooling, including Kubernetes, Docker, CI/CD, and git-based workflows
Familiarity with cloud platforms (AWS) and cloud data warehouses (Snowflake/SQL)

Benefits

Salary: to be discussed, depending on experience

Length: 12 months, with the view to extend

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About the Company

G MASS

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Company stage: Not listed
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