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

Apply Now

Senior Data Scientist

BBC
Salford
3 days ago
Create job alert
Job Details

Job band: D
Contract type: Permanent, Full‑time
Department: BBC R&D
Location: London – Hybrid, Manchester – Hybrid
Proposed salary range: up to £69,700, depending on relevant skills, knowledge and experience.
We’re also happy to discuss flexible working; you may indicate your preference in your application. Flexible working will be part of the discussion at the offer stage.


Purpose of the Role

BBC R&D is hiring a Senior Data Scientist to join our AI Research team, working at the forefront of AI, news and broadcasting. You’ll tackle complex, high‑impact challenges – from automated fact verification to contextual language understanding and developing editorially aligned LLMs – and see your work embedded in BBC News products, journalistic workflows and editorial decision‑making.


Key Responsibilities and Impact

  • Innovate and solve complex problems: apply creative thinking and sound judgment to develop innovative solutions to technical challenges, often exploring diverse approaches.
  • Prototype and evaluate technologies: design, build and test prototypes to explore research questions and assess new technologies through trials, lab tests and user feedback.
  • Drive impact through knowledge transfer: translate research outcomes into practical solutions for the BBC or broader industry via operational systems, open‑source contributions or standards input.
  • Collaborate across and beyond the BBC: work closely with internal teams and external partners, including universities and manufacturers, to deliver impactful results on joint projects.
  • Communicate and represent the BBC: share findings through internal documentation, external publications and research conferences.

Essential Criteria – Skills and Experience

  • Bachelor’s, Master’s or Ph.D. in Machine Learning, Computer Science or a closely related discipline (or equivalent practical experience).
  • Hands‑on experience with cutting‑edge NLP models (GPT family, Google Gemini, Claude, BERT) and proficiency in frameworks such as PyTorch, TensorFlow and Scikit‑Learn.
  • Excellent Python coding abilities and familiarity with software engineering best practices.
  • Proven track record in designing experiments, building training pipelines and translating business needs into technical solutions.
  • Competence in cloud deployment, data management and MLOps workflows, including versioning and performance evaluation.

Desired but Not Required

  • Experience deploying systems to Azure and/or OpenStack, with a strong grasp of cloud‑native architectures and best practices.
  • Proven web application development experience, from building responsive UIs to integrating complex back‑ends.
  • Experience managing and evolving large‑scale codebases with a focus on maintainability, scalability and clean architecture.
  • Collaborative approach to innovation, working closely with Data Scientists, ML Engineers and researchers to translate cutting‑edge ideas into production‑ready solutions.
  • Academic background, participation in published documents within your area of specialism.

Before your start date, you may need to disclose any unspent convictions or police charges, in line with our Contracts of Employment policy. Failure to disclose may result in withdrawal of your offer.


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior 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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.