Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Principal Engineer – Data Science

GE Vernova
Stafford
3 days ago
Create job alert
Principal Engineer – Data Science

GE Vernova


Overview

The Principal Engineer – Data Science combines a high level of technical expertise with sound business acumen and a strong understanding of engineering processes. Principal Engineers are part of a formal career path for technical personnel who want to continue to develop and grow their technical competencies while having increasing impact on the business.


Responsibilities

  • Lead technical direction for large projects during contract execution phase.
  • Support Consulting Engineers in business line technology strategy definition and Multi-Generational Product Plan (MGPP).
  • Chair design reviews for individual components, sub-assemblies and key engineering deliverables at tendering and contract execution stages.
  • Provide key technical consultation on product problems throughout the business, including supplier and field support and perform technical rescues when needed.
  • Participate in Patent Evaluation Board (PEB) to protect technology that gives the business a competitive advantage.
  • Represent the business externally at conferences or in professional working bodies (IEC, CIGRE etc) and maintain active relationships with relevant academic institutions.
  • Lead early research and proof-of-concepts for promising technology applications.
  • Provide ad-hoc technical guidance to the Engineering/Technology leadership team as required, e.g., joining customer negotiations or supplier audits.
  • Develop technical competencies by establishing and delivering structured technical training schemes within one’s own business lines.
  • Mentor and coach identified high potential Engineering talents within one’s business lines.

Qualifications & Requirements

  • Master of Science in Computer Science, Machine Learning, Engineering, or Mathematics.
  • At least 10 years of experience in an engineering or data science capacity.
  • Experience with state-of-the-art machine learning technologies & techniques in at least one of the following domains: Natural Language Processing, Time Series, Computer Vision.
  • Strong oral and written communication skills.
  • Strong interpersonal and leadership skills.
  • Problem analysis and resolution skills.
  • Ability to work across organizations in a matrix environment.
  • Preferably having taken a Senior Engineer or Senior Researcher role.
  • Able to interface effectively with most levels of the organization.
  • Able to pursue Engineering integrity in adverse conditions.
  • Lean experience preferred.

Additional Information

Relocation Assistance Provided: No.


This is a remote position.


#J-18808-Ljbffr

Related Jobs

View all jobs

Principal Data Engineer

Principal Data Engineer

Principal Data Engineer

Principal Data Scientist - AI

Clinical Data Engineer

Clinical Data Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.