Data Scientist

BBC Studios Ltd
Birmingham
1 day ago
Create job alert
Key Responsibilities and Accountabilities

  • Designs experiments, test hypotheses, and build models.
  • Translates business challenges into machine learning problems.
  • Develops machine learning algorithms to problems that don’t always have existing textbook solutions.
  • Collaborates with subject matter experts to select the relevant sources of data from across BBC divisions.
  • Works with team leaders and members to solve data science problems and documents results and methodologies.
  • Works in iterative processes and validates findings, performs experimental design approaches to validate findings or test hypotheses.
  • Validates analysis by comparing appropriate samples, employs the appropriate algorithm to discover patterns.
  • Uses the expected qualification and assurance of the data to quantify the accuracy metrics of the analysis.
  • Qualifies where information can be stored or what data, external to the organisation, may be used in support of the use case.
  • Assesses the volume of data supporting the initiative, the type of data (e.g., images, audio, video, text, clickstream etc.) and the speed or sudden variations in data collection.
  • Collaborates with the data engineer to ensure that the data used follows compliance, access management, and control policies and that it meets the qualification and assurance requirements.
  • Recommends ongoing improvements to methods and algorithms that lead to findings, including new information.
  • Spreads the value of data and data science across the wider organisation.

Disclaimer

This job description is a written statement of the essential characteristics of the job, with its principal accountabilities, incorporating a note of the skills, knowledge and experience required for a satisfactory level of performance. This is not intended to be a complete, detailed account of all aspects of the duties involved.


Please note: If you were to be offered this role, the BBC will conduct Employment screening checks which include Reference checks; Eligibility to work checks; and if applicable to the role, Safeguarding and Adverse media/Social media checks. Any offer made is conditional on these checks being satisfactory.


The BBC is committed to redeploying employees seeking suitable alternative employment within the BBC and they will be given priority consideration ahead of other applicants. Priority consideration means for those employees seeking redeployment their application will be considered alongside anyone else at risk of redundancy, prior to any individuals being considered who are not at risk.


Information at a Glance


This is your BBC

At the BBC you can create and innovate in an inclusive environment while contributing to some of the world’s best loved content, and the BBC’s mission to inform, educate and entertain.


Find out more about the BBC


Life at the BBC

Here you will benefit from:



  • Fair pay and flexible benefits including a competitive 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.
  • Excellent career and professional development.
  • Support in your working life, including flexible working which you can discuss with us at any point during the application, selection or offer.
  • A values‑based organisation where the way we do things is important as what we do.

Benefits may vary if you are joining on an FTC basis or on an orchestra conditions contract.


Learn more about life at the BBC and our values in our candidate pack.


Candidate pack
You belong

We have a working environment where we value and respect every individual's unique contribution, so all our employees feel that they can belong, thrive and achieve their full potential.


We want to attract the broadest range of talented people to join us. The more diverse our workforce, the better able we are to respond to and reflect our audiences in all their diversity.


We welcome applications from individuals, regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio‑economic background, religion and/or belief.


Find out more about diversity, inclusion and belonging in our strategy below.


Disability confident

We are a disability confident employer. If you need to discuss adjustments or access requirements for the interview process, or to carry out this role, please contact us via email and we’d be happy to discuss:


BBC Group and Public Services, Broadcasting House, Portland Place, London, United Kingdom, W1A 1AA. BBC Studios Distribution Limited, company no: 01420028, registered address: 1 Television Centre, 101 Wood Lane, London, United Kingdom W12 7FA.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.