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

Apply Now

Lead Data Scientist - NLP

NLP PEOPLE
Guildford
3 days ago
Create job alert
Overview

Role Description: In this role you will collaborate with data scientists, ML engineers, and analysts to design and implement solutions that extract insights from unstructured text. Projects may include topic modelling and entity recognition, text generation, ontology creation, and conversational AI. This role demands strong technical expertise in NLP and a passion for innovation and problem-solving.

This is a hybrid role based in our Guildford office, with the expectation to be on-site one day per week.

Salary Information

Pay is based on relevant experience, skills for the role, and location. Salary is only one part of our total reward package.

About You

In this role you will be:

  • Implementing enterprise-level ML/GenAI strategies aligned with business objectives.
  • Designing and developing NLP solutions using techniques such as text classification, NER, topic modeling, and text generation.
  • Collaborating with data engineers and MLOps teams to ensure clean data pipelines and seamless model integration.
  • Applying LLMs (e.g., GPT, BERT, LLaMA2) and knowledge graphs to enhance natural language understanding and reasoning.
  • Conducting exploratory data analysis and developing robust pre-processing strategies for unstructured text.
  • Evaluating NLP models, algorithms, and architectures to recommend optimal solutions.
  • Communicating insights through reports, visualizations, and presentations for both technical and non-technical audiences.
  • Mentoring junior team members and staying current with advances in NLP, LLMs, and GenAI.
Essential Skills
  • A Bachelor's or Master's degree in Computer Science, Data Science, or a related field (or equivalent combination of education and experience).
  • Proven experience as a Data Scientist with a strong focus on NLP and knowledge graphs.
  • Practical experience with graph machine learning techniques such as graph neural networks and graph data science.
  • Proficiency in Python and experience with key NLP libraries and frameworks (e.g., NLTK, spaCy, Transformers, Hugging Face, BERT, Langchain).
  • Hands-on experience with deep learning frameworks like TensorFlow or PyTorch.
  • Strong problem-solving skills, particularly in addressing complex NLP challenges.
  • Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.
Desirable Skills
  • Experience using Azure OpenAI and foundational LLMs for Retrieval-Augmented Generation (RAG), including open-source implementations.
  • Experience with LLM architecture (e.g., Transformer, GANs, VAEs), Fine-tuning PEFT/LoRA, Context embedding, Vector database technologies, and Semantic Search techniques & tools.
  • Familiarity with Azure cloud platform services and distributed computing frameworks (e.g., SparkNLP) is a plus.
  • Knowledge of Agentic workflows and frameworks.
  • Experience extracting data from PDFs using Python libraries (e.g., PyPDF, Camelot) and OCR tools like Azure Document Intelligence or AWS Textract.
  • Awareness of emerging InsurTech innovations, with the ability to propose creative solutions and adapt open-source models and technologies.
What We Will Offer You

Recognised and rewarded for a job well done, we have a range of flexible benefits for you to choose from- so you can pick a package that's perfect for you. We also offer flexible working options, global career opportunities across the wider Allianz Group, and fantastic career development and training. That's on top of enjoying all the benefits you'd expect from the world's number one insurance brand, including:

  • Flexible buy/sell holiday options
  • Hybrid working
  • Annual performance related bonus
  • Contributory pension scheme
  • Development days
  • A discount up to 50% on a range of insurance products including car, home and pet
  • Retail discounts
  • Volunteering days
Our Ways of Working

Do you need flexibility with the hours you work? Let us know as part of your application and if it's right for our customers, our business and for you, then we'll do everything we can to make it happen. We believe in supporting hybrid work patterns, which balance the needs of our customers, with your personal circumstances and our business requirements.

Integrity, Fairness, Inclusion & Trust

At Allianz, we believe in fostering an inclusive workforce and are proud to be an equal opportunity employer. Our commitment to equal opportunities, gender equity, and balanced gender representation, is demonstrated by our numerous accreditations.

Accessibility and Applications

We support candidates with disabilities or long-term health conditions through the Offer an Interview Scheme, for those meeting the essential skills for the role.

We are an equal opportunities employer and welcome applications from all qualified candidates.


#J-18808-Ljbffr

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist - Remote

Lead Data Scientist - Remote

Lead Data Scientist - Remote

Lead Data Scientist

Lead Data Scientist

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.