Senior Software Engineer, BBC Verify

BBC Group and Public Services
London
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist - NLP AI Research

Senior Data Scientist - NLP AI Research

Senior Software & Data Engineer (Java/Python)

Senior AI Software Engineer - Gen AI & NLP

Senior Data Engineer - Advertising Tech

Lead Software Engineer - Agentic AI/Machine Learning

Senior Data Scientist (Senior Software Engineer), BBC Verify Permanent - Full TimeLocation: London, GB, W1A 1AAJob Reference:

20803

Hit Apply below to send your application for consideration Ensure that your CV is up to date, and that you have read the job specs first.Band:

DSalary:

Up to £70,000 plus London weighting depending on relevant skills, knowledge and experience.Contract type:

PermanentLocation:

London - HybridWe’re happy to discuss flexible working. Please indicate your choice under the flexible working question in the application.Excellent career progression:

The BBC offers great opportunities for employees to seek new challenges and work in different areas of the organisation.Unrivalled training and development opportunities:

Our in-house Academy hosts a wide range of internal and external courses and certification.Benefits:

We offer a negotiable salary package, a flexible 35-hour working week, 25 days annual leave with the option to buy an extra 5 days, a defined pension scheme, and discounted dental, health care, and gym.BBC Verify brings together open-source intelligence (OSINT) specialists, disinformation reporters, fact checkers, and data journalists to find impactful stories and offer new lines of compelling coverage across BBC News’s platforms.The successful candidate will work alongside three other data scientists, using their computing skills to enhance the story-finding and storytelling capacity of BBC Verify.Main Responsibilities

This role requires a data scientist or programmer who specializes in ingesting, analyzing, and explaining satellite data and can work effectively in news as part of a multidisciplinary team.The candidate’s computing skills will enable the team to find new stories, select which story ideas to pursue, and find stories faster from satellite imagery and APIs.Examples of recent projects include:Automating steps in the collection and presentation of satellite imagery to speed the assessment of damage in military conflict.Are you the right candidate?

The successful candidate must have:Extensive experience finding newsworthy insights in a broad range of satellite data sources.Extensive experience developing automated tools to find stories in satellite data.Experience embedding the latest research practices/techniques relating to satellite data in non-expert groups.Extensive experience programming in R and/or Python for automating the extraction, cleaning, transformation, and analysis of satellite data.An understanding of delivering stories or projects with editorial content to tight deadlines.Experience identifying, proposing, and delivering new ideas for analysis or improvements to workflows based on technical skills and understanding of the organisation’s needs.Strong communication skills to work with non-technical colleagues to identify strengths and weaknesses of story ideas and explain analysis findings clearly and concisely.About the BBC

The BBC is committed to redeploying employees seeking suitable alternative employment within the BBC for different reasons and will give priority consideration to them.We value our values and behaviours, which are important to us. Please make sure you’ve read about our values and behaviours.Diversity matters at the BBC. We value and respect every individual's unique contribution, enabling all of our employees to thrive and achieve their full potential.We want to attract the broadest range of talented people to be part of the BBC. The more diverse our workforce, the better we can respond to and reflect our audiences.We are committed to equality of opportunity and welcome applications from individuals, regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio-economic background, religion, and/or belief.DISCLAIMER This job description outlines the essential characteristics of the job, including principal accountabilities and the skills, knowledge, and experience required for satisfactory performance. It is not intended to be a complete account of all aspects of the duties involved.

#J-18808-Ljbffr

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.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Jobs

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.