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

Apply Now

Principal Data Scientist

Xcede
City of London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist

Principal Data Scientist, AI Security Research

Principal Data Scientist- CPG

Principal Data Scientist

Principal Data Scientist

Principal Data Scientist

Principal Data Scientist

(Hybrid work in London - roughly 1/2 days a week in London)


We’re partnered with a well-established, tech-driven company that’s redefining how data science supports product innovation and customer experience across its domain. With a strong focus on automation, personalisation, and intelligent decisioning, this organisation is placing data science at the heart of its strategy, investing heavily in both classic ML and next-generation AI capabilities.

This is a company that blends modern product engineering with a fast-moving, high-ownership culture. Their environment encourages experimentation, cross-functional collaboration, and the freedom to shape what “great” looks like in machine learning at scale.


The Role

As a Principal Data Scientist, you’ll play a pivotal role in shaping the company’s ML capability, both technically and culturally. Working alongside a collaborative data science team, you’ll help embed AI into core product workflows, define best practices across model deployment and experimentation, and support the evolution of their real-time modelling infrastructure.

This is a hybrid hands-on / leadership role, ideal for someone who thrives at the intersection of applied research, platform integration, and engineering-minded data science.


What You’ll Be Doing

  • Build and deploy a wide variety of models spanning classification, regression, propensity scoring, and LLM-based use cases
  • Spearhead the entire company’s GenAI efforts. The team have multiple LLM projects running but would love a technical leader in this area
  • Own the end-to-end lifecycle of ML projects, from feature engineering to deployment and monitoring
  • Define best practices for model testing, automation, and continuous improvement within a high-performing team
  • Act as a technical thought leader, partnering with stakeholders across Product, Engineering, and Analytics
  • Drive the adoption of real-time decisioning systems and champion the operationalisation of AI
  • Support the upskilling of the wider organisation in modern ML practices, helping teams unlock greater value from data
  • Lead by example in establishing a sustainable, scalable approach to AI delivery


What We’re Looking For

  • 6-10+ years’ experience as a Data Scientist or ML Engineer, with exposure to both traditional ML and generative AI
  • Strong belief in data science as a product, not just a modelling function
  • Clear evidence of commercially successful / or centrally impactful LLM based work for previous companies.
  • Proven track record deploying real-time models into production environments
  • Technical depth in Python, software engineering principles, and deployment tooling
  • Familiarity with experimentation frameworks and model monitoring approaches
  • A pragmatic mindset, able to balance rigour with delivery, and guide stakeholders toward value
  • Prior experience building ML capabilities within product-focused teams or high-growth environments
  • Interest in shaping team norms, mentoring others, and elevating data science maturity


Package includes:

  • Competitive salary, annual performance bonus, and strong long-term incentives
  • Comprehensive benefits including private health cover, enhanced leave policies, and personal development support
  • Additional perks such as flexible benefits allowance, paid sabbaticals, mental health support, and lifestyle benefits (e.g. car scheme, dental, gym, and more)


If this role interests you and you would like to find out more (or find out about other roles), please apply here or contact us via (feel free to include a CV for review).

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.