Principal Data Scientist I - Agentic Systems

LexisNexis
City of London
5 days ago
Create job alert

LexisNexis Intellectual Property Solutions is seeking a Principal Data Scientist specialising in Agentic Systems, with deep expertise in efficient context engineering, fine-tuning large language models (LLMs), and multi-agent workflows. This hands‑on role focuses on pushing the boundaries of how intelligent systems plan, reason, and act in real-world intellectual property (IP) applications. As part of our dynamic Data Science and AI Engineering community, you'll have the opportunity to rotate across diverse problem domains, gaining broad expertise while delivering impact. You'll partner closely with product managers and commercial teams to understand the workflows of IP professionals, and collaborate with engineers to prototype, evaluate, and optimise intelligent agents that drive measurable customer value.


Responsibilities

  • Design, build, and optimise agentic systems capable of breaking down complex tasks into executable steps.
  • Innovate in context engineering strategies (retrieval-augmented generation, dynamic context construction, memory management) to maximise LLM effectiveness.
  • Select, fine‑tune, and adapt large language models (LLMs) to align with agent objectives.
  • Enhance and extend evaluation frameworks for online and offline performance, efficiency, and success rates.
  • Select, benchmark, and host LLMs on Amazon SageMaker or EKS, comparing and optimising models to ensure agentic systems are fast, accurate, and cost-effective.
  • Own and prioritise a backlog of improvements to core intelligence, applying methods such as RLHF and self‑learning agents.
  • Collaborate with AI engineering and product teams to deliver production‑ready solutions.
  • Mentor and supervise junior data scientists and foster a culture of innovation, collaboration, and continuous improvement.

Qualifications

  • PhD in AI, Computer Science, Mathematics or a related field; or Master’s with equivalent experience.
  • Hands‑on experience in LLMs or applied AI research in a commercial environment.
  • Proven expertise in context engineering, LLM fine‑tuning, and evaluation of non‑deterministic systems.
  • Strong programming skills in Python and proficiency in deep learning frameworks (e.g., PyTorch).
  • Strong problem‑solving, analytical, and communication skills.
  • Demonstrated ability to work collaboratively with engineering and product stakeholders in an agile environment.

Company Information

LexisNexis Intellectual Property, which serves customers in more than 150 countries with 11,300 employees worldwide, is part of RELX, a global provider of information‑based analytics and decision tools for professional and business customers.


At LexisNexis Intellectual Property (LNIP), we believe that whenever a person works on a patent and understands the future trajectory of a specific technology, that person has the potential to fundamentally change how society operates. We are proud to directly support and serve these innovators in their endeavours to better humankind. We enable innovators to accomplish more by helping them make informed decisions, be more productive, comply with regulations, and ultimately achieve superior results. By harnessing the latest advances in machine learning combined with expert analysis, LexisNexis Intellectual Property is disrupting how actionable insight is extracted from patent data. Information can now be accessed with efficiency, accuracy and at a speed that is just not possible by traditional methods. Our overall success is measured by how well we deliver these results.


Join our team and contribute to a culture of innovation, collaboration, and excellence. If you are ready to advance your career and make a significant impact, we encourage you to apply.


Benefits and Working Flexibility

We promote a healthy work/life balance across the organisation. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long‑term goals.



  • Working flexible hours – flexing the times when you work in the day to help you fit everything in and work when you are the most productive.
  • Generous holiday allowance with the option to buy additional days.
  • Life assurance.
  • Access to a competitive contributory pension scheme.
  • Save As You Earn share option scheme.
  • Travel Season ticket loan.
  • Maternity, paternity, and shared parental leave.
  • Employee Assistance Programme.
  • Access to emergency care for both the elderly and children.
  • RECARES days, giving you time to support the charities and causes that matter to you.
  • Access to employee resource groups with dedicated time to volunteer.
  • Access to extensive learning and development resources.
  • Access to the employee discounts scheme via Perks at Work.


#J-18808-Ljbffr

Related Jobs

View all jobs

Principal Data Scientist I - Agentic Systems

Principal Data Scientist: ML Leader, Mentor, Hybrid Role

Principal Data Scientist London, United Kingdom

Principal Data Scientist: Agentic AI Systems Lead

Principal Data Scientist

Principal Data Scientist - Agentic AI & LLM Engineering

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.