Lead Data Scientist - Production ML & Predictive Analytics

Xpertise Recruitment

Greater London

Hybrid

GBP 111,000 - 129,000

Full time

7 days ago
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Job summary

Xpertise Recruitment is seeking a Principal Data Scientist to lead a major Predictive Maintenance programme in West London, combining hands-on ML delivery with technical leadership. You will mentor a growing team of data scientists and shape production-grade ML solutions that reduce operational risks and improve aircraft availability.

You’ll partner with Product, Engineering and Data teams to deliver measurable business value, drive best practices, and scale the Data Science capability across a

Qualifications

  • Principal Data Scientist or equivalent with production ML experience.
  • Strong software engineering mindset and coding standards.
  • Experience mentoring and developing technical teams.
  • Background in product-led tech environments.
  • MLOps and model lifecycle management experience.
  • Cloud platforms, ideally AWS.

Responsibilities

  • Design, build, deploy and manage production ML models.
  • Solve predictive maintenance and operational risk challenges.
  • Define best practices across ML and software engineering.
  • Lead code reviews and drive technical excellence.
  • Mentor junior data scientists.
  • Partner with Product and Engineering teams to deliver measurable outcomes.
  • Support MLOps, deployment and productionisation.
  • Scale and mature a growing Data Science capability.

Skills

Python
AWS
MLOps
Leadership
Production ML
Mentoring
Model deployment

Tools

MLOps tooling

Job description

Xpertise Recruitment is seeking a Principal Data Scientist to lead a major Predictive Maintenance programme in West London, combining hands-on ML delivery with technical leadership. You will mentor a growing team of data scientists and shape production-grade ML solutions that reduce operational risks and improve aircraft availability.

You’ll partner with Product, Engineering and Data teams to deliver measurable business value, drive best practices, and scale the Data Science capability across a

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