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
1 month 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 & Data Platform Leader (Hybrid)

Principal Data Engineer

Principal Data Engineer

Principal Data Engineer

Contract Principal Data Engineer - eDV Cleared

Contract Principal Data Engineer - eDV Cleared

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.

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

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.