Senior Software Engineer, BBC Verify

British Broadcasting Corporation
London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Software Engineer (GO/PHP)

Lead / Senior Software Engineer - ML/AI

Senior Software Engineers

Software Engineer

Principal Software Engineer

Software Engineer

Senior Data Scientist (Senior Software Engineer), BBC Verify

Join to apply for theSenior Data Scientist (Senior Software Engineer), BBC Verifyrole atBBC.

Package Description
Job Reference:20803
Band:D
Salary:Up to £70,000 plus London weighting depending on relevant skills, knowledge and experience. The expected salary range for this role reflects internal benchmarking and external market insights.
Contract type:Permanent
Location:London - Hybrid
We’re happy to discuss flexible working. Please indicate your choice under the flexible working question in the application. There is no obligation to raise this at the application stage but if you wish to do so, you are welcome to. Flexible working will be part of the discussion at offer stage.

  • 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 for work-life balance and 25 days annual leave with the option to buy an extra 5 days, a defined pension scheme and discounted dental, health care and gym.

If you need to discuss adjustments or access requirements for the interview process please contact the . For any general queries, please contact: .

Job Introduction

BBC Verify brings together open-source intelligence (OSINT) specialists, disinformation reporters, fact checkers and data journalists to find impactful stories and to offer new lines and compelling coverage of the biggest issues across BBC News’s website, social media, television and radio. The successful candidate will work alongside three other data scientists, using their computing skills to multiply the story-finding and story-telling capacity of BBC Verify.

Main Responsibilities

This is a role for a data scientist, programmer, or a mix of both who specialises in ingesting, analysing and explaining satellite data and who can work effectively in news as part of a multidisciplinary team. Their computing skills will enable the team to find new stories, to pick which story ideas to follow and to find stories faster from satellite imagery, APIs.

Examples Of Recent Projects Include

  • Automating steps in the collection and presentation of satellite imagery in order to speed the assessment of damage in military conflict.

The Successful Candidate Must Have

  • Extensive experience finding newsworthy insights in a broad range of satellite data sources.
  • Extensive experience developing automated tools that have the potential to find stories in satellite data.
  • Experience of embedding the latest research practices/techniques relating to use of satellite data in non-expert groups.
  • Extensive experience programming in R and/or Python to automate 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.
  • The communication skills to work with non-technical colleagues to identify the technical strengths and weaknesses of story ideas and to explain analysis findings and caveats clearly and concisely.

About The BBC

The BBC is committed to redeploying employees seeking suitable alternative employment within the BBC for different reasons and they will be given priority consideration ahead of other applicants. We don’t focus simply on what we do – we also care how we do it. Our values and the way we behave are important to us. Please make sure you’ve read about our values and behaviours here. Diversity matters at the BBC. We have a working environment where 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 – whether that’s to contribute to our programming or our wide range of non-production roles. The more diverse our workforce, the better able we are to respond to and reflect our audiences in all their diversity.

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. We will consider flexible working requests for all roles, unless operational requirements prevent otherwise.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.