Principal Data Scientist

BBC
Glasgow
6 months ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Principal Data Engineer

Principal Data Engineer (GCP)

Principal Data Engineer (MS Azure)

Principal Data Engineer (GCP)

Principal Data Engineer (GCP)

Job Description This job is with BBC, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.
Job Details Job Band: D
Contract Type: Permanent, Full-time
Department: BBC Product Group, Discoverability (Search)
Location: London / Newcastle / Salford / Glasgow - Hybrid working with 1 day a week expected in office base location.
Salary: £72,000 - £82,000 depending on relevant skills, knowledge and experience. The expected salary range for this role reflects internal benchmarking and external market insights.
We're happy to discuss flexible working. If you'd like to, please indicate your preference in the application - though there's no obligation to do so now. Flexible working will be part of the discussion at offer stage.
Purpose Of The Role The BBC has been serving audiences online for decades, across key products such as BBC iPlayer. As we evolve to deliver more personalised content and experiences, Data Science is at the heart of that transformation.
As a team, we use ML / AI to enrich our content and power personalised experiences for millions of audience members. We're looking for a Principal Data Scientist to join the Product Group.
Why Join The Team As Principal Data Scientist you'll play a hands-on role in building machine learning products at BBC scale. Working as part of a highly cross-functional team, you'll help overcome the challenges of deploying ML in production.
You'll have the opportunity to get involved with the wider data science community, both at the BBC and externally. We hope you'll be enthusiastic about sharing your knowledge and growing others.
Your Key Responsibilities And Impact You'll use your technical skills to deliver value to BBC audiences, blending significant breadth and depth of data science expertise.
You'll have impact within your immediate team and beyond, across the wider BBC, instrumental in developing scalable ML products.
You'll bring significant experience of being an effective contributor in a cross-functional team, working with others to overcome the challenges of delivering ML in production.
You'll be responsible for using your extensive knowledge of machine learning algorithms to solve complex problems effectively.
You'll join the wider BBC Data Science community, with internal and external opportunities to get involved, share your knowledge and shape the ecosystem.
Your Skills And Experience Essential Criteria:
A strong understanding of data science and machine learning techniques, including recent advances and their applications for implementation in a production environment.
Strong working knowledge of data science best practice, including working with cloud services and strong coding skills, particularly in Python, including knowledge of code management and deployment.
A proven track record of delivering value in production.
The ability to contribute effectively in a cross-functional team, including the ability to prioritise and work in a structured manner to ensure timely delivery while balancing quality, cost and speed.
Ability to clearly communicate to both technical and non-technical audiences, both regarding short-term decisions and longer-term strategy.
Desired But Not Required:
If you can bring some of these skills and experience, along with transferable strengths, we'd love to hear from you and encourage you to apply.
Strong understanding and experience of development and productionisation of data science products in the Search domain, for example, information retrieval.
Strong understanding and experience of experimentation within data science.
Experience with model lifecycle management and MLOps, particularly within AWS.
Experience of supporting other Data Scientist/s with their technical work to deliver value in production.
#LI-DNI

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.