Data Scientist and AI Engineer on KTP Project with Carpenters Group

University of Liverpool
Liverpool
2 months ago
Applications closed

Related Jobs

View all jobs

Applied AI, Forward Deployed Machine Learning Engineer - EMEA

DV Cleared Data Scientist | GenAI & NLP Lead (Manchester)

AI & Data Engineer

Senior AI/Machine Learning Engineer

Machine Learning Manager, Munich

Machine Learning Researcher

Data Scientist and AI Engineer on KTP Project with Carpenters Group

We are seeking to appoint a highly qualified and motivated individual to undertake a role as a data scientist and AI engineer, working on an Innovate UK funded, three-year collaborative project between Carpenters Group and the School of Computer Science and Informatics. Carpenters are a market‑leading insurance legal services provider. Within Carpenters, and a team from the School, you will realise a programme of work to automate a number of legal processes and build tools involving AI technologies related to Machine Learning, Natural Language Processing, Neurosymbolic Computing and Explainable AI. You will work within a research‑based commercial environment to build a commercial product based on the state‑of‑the‑art AI and data science technology.


Responsibilities

  • Lead the design and implementation of AI solutions for legal process automation.
  • Conduct high‑quality research to develop novel machine learning, NLP, and neuroscience‑inspired models.
  • Translate research outcomes into robust software engineering practices and production‑ised products.
  • Collaborate with cross‑functional teams across Carpenters and the School of Computer Science and Informatics.
  • Advise on AI governance, ethics, and explainability requirements.

Qualifications

  • At least an MSc in computer science or closely related subject; PhDs preferred.
  • Demonstrated experience conducting high‑quality computer science research.
  • Strong software engineering and programming skills.

Compensation and Benefits

The salary will be enhanced by a substantial annual training budget to support the post holder’s professional development. The post is available for 3 years and is based at Carpenters office in Liverpool.


Diversity Commitment

The University of Liverpool is committed to enhancing workforce diversity. We actively seek to attract, develop, and retain colleagues with diverse backgrounds and perspectives. We welcome applications from all genders/gender identities, Black, Asian, or Minority Ethnic backgrounds, individuals living with a disability, and members of the LGBTQIA+ community.


Position Details

  • Seniority level: Entry level
  • Employment type: Full‑time
  • Job function: Engineering and Information Technology
  • Industry: Higher Education


#J-18808-Ljbffr

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.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.