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

Apply Now

Principal Data Scientist - AI

Opus 2
City of London
3 days ago
Create job alert
Overview

We\'re looking for a Principal Data Scientist to lead the design of AI systems that can support complex, multi-step tasks, use tools, adapt to context, and make intelligent decisions. You\'ll work closely with product and engineering to architect LLM-powered features that are robust, useful, and production-ready not just impressive demos. This role is ideal for someone who not only understands data and AI deeply but also brings strong software engineering instincts. Someone who can design intelligent systems that operate reliably and scalably within a complex product environment.


What you\'ll be doing

  • You will explore and implement RAG or Graph RAG approaches to improve retrieval quality and reasoning in LLM workflows, including graph construction, entity linking, and hybrid scoring strategies.
  • Design and implement LLM-powered systems that support multi-step task automation, tool use, and context-aware reasoning within the product.
  • Contribute to prompt design and evaluate prompts results
  • Design and implement MLOps pipelines with best practises in mind
  • Prototype, evaluate, and ship AI features using platforms like Claude or Amazon Bedrock with a focus on production readiness, not just experimentation.
  • Develop evaluation frameworks for testing model output quality, reliability, and alignment with user goals (e.g., hallucination detection, prompt regression, safety scoring).
  • Work closely with engineers and designers to embed AI capabilities into real user workflows, ensuring that technical architecture supports meaningful product outcomes.
  • Own the full lifecycle of AI components from evaluation frameworks, planning and modeling to testing, deployment, and continuous improvement.
  • Contribute to our AI strategy and roadmap, helping shape how we scale LLM usage responsibly across the platform. You bridge product and engineering by embedding AI into user-facing features, not just prototypes.
  • Collaborate closely with Principal Engineer to align AI workflows, system prompts, and model interaction layers.

What excites us?

We\'ve moved past experimentation. We have live AI features and a strong pipeline of customer\'s excited to get access to more improved ai powered workflows. Our focus is on delivering real, valuable AI-powered features to customers and doing it responsibly. You\'ll be part of a team that owns the entire lifecycle of these systems: planning, building, testing, deploying, and evolving them as part of our core product.


Requirements
What we\'re looking for in you

  • You\'re a practical AI builder. Not just a theorist. You think in terms of customer value, vs. research or academic novelty.
  • You\'ve worked with Claude and/or Amazon Bedrock (or similar LLM platforms) and understand prompt design, model behavior tuning, and output evaluation.
  • You\'re comfortable writing code, reviewing PR\'s, and deliver a reliable, explainable, production-ready product.
  • Are excited about MLOps and brings experience in implementing MLOps best practices.
  • You\'re curious about how people interact with AI and want to build systems they can trust and understand.

Qualifications

  • Experience fine tuning models.
  • Ability to reason about multi-hop question answering, graph traversal, and integrating structured retrieval with LLM prompts.
  • Performance over time.
  • 5+ years experience in applied AI, machine learning systems, or data science roles with production responsibility.
  • Proficiency in Python, with experience building and maintaining AI systems, not just analyses.
  • Experience working in Java or TypeScript environments beneficial.
  • Experience with Claude, OpenAI, Bedrock is nice to have or experience with similar LLM platforms required.
  • Familiarity with RAG, Graph-based retrieval, prompt design, and multi-hop reasoning.
  • Experience deploying LLM-powered features into production environments.
  • Bonus: experience in vector stores, agent orchestration, or legaltech domain knowledge.

Benefits
Working for Opus 2

Opus 2 is a global leader in legal software and services, trusted partner of the world\'s leading legal teams. All our achievements are underpinned by our unique culture where our people are our most valuable asset. Working at Opus 2, you\'ll receive:



  • Contributory pension plan.
  • 26 days annual holidays, hybrid working, and length of service entitlement.
  • Health Insurance.
  • Loyalty Share Scheme.
  • Enhanced Maternity and Paternity.
  • Employee Assistance Programme.
  • Electric Vehicle Salary Sacrifice.
  • Cycle to Work Scheme.
  • Calm and Mindfulness sessions.
  • A day of leave to volunteer for charity or dependent cover.
  • Accessible and modern office space and regular company social events.


#J-18808-Ljbffr

Related Jobs

View all jobs

Principal Data Scientist I - Agentic Systems

Principal Data Scientist

Principal Data Scientist

Principal Data Scientist

Principal Data Scientist

Principal 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.