Senior Data Scientist (12 Month FTC)

DEPOP
London
7 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Role

This is a 12 month fixed term contract role.

The strategy analytics team works closely with strategy, strategic finance and executive stakeholders to support on strategic projects and company level reporting.

This includes leading on company level metrics, translating ambiguous strategic questions into practical analysis and communicating these results to a senior set of stakeholders. This is to ensure that the management team has a strong basis of understanding of company performance based on data. It is also to ensure we are able to make accurate and timely forecasts of company performance and be able to explain deviations in a structured way. Finally, it is to ensure that senior stakeholders are supported with their strategic questions with well articulated analysis. These are all in the support of making the best strategic decisions to enable the company to deliver on user experience and business goals.

This role requires a high level of independence and working directly with senior stakeholders to solve challenging strategic problems. Expertise in choosing the right method from a broad set of analytical methods under the constraints of time and data availability is crucial.

Responsibilities

  • Strategic Analysis: work closely with the S&O, Strategic Finance and Exec teams to lead analytics on ambiguous strategic questions with a high level of independence, including supporting special projects. This will sometimes require expansion of responsibilities into new domains and supporting new growth opportunities and/or cross functional teams.
  • Lead company level reporting: lead on executing the monthly and quarterly company level reporting. This will require a combination of building core Looker and Slide outputs as well as efficient short deep dives into key trends, risks and opportunities.
  • Finance team support: be a go-to contact in the Insights team for the finance team.
  • Collaborate with product teams: this role will likely require adaptability to work directly with product teams to help with discrete analysis.

Requirements

  • Exemplary problem solving skills, with particular strength in creating meaningful analysis from ambiguous questions.
  • Strong competency in verbal and written communication of results to a wide variety of stakeholder levels.
  • A high degree of independence and ability to manage upwards to senior stakeholders.
  • Ability to build relationships across a wide range of stakeholder teams and leverage their knowledge to answer higher level strategic questions.
  • Thrives when working across multiple projects.
  • Aptitude and resourcefulness to work in new domains.
  • Expertise in SQL and the ability to work with large datasets.
  • Expertise in visualisation tools like Looker or Tableau.
  • Experience in Python and command over ETL scripts.
  • Deep commercial awareness and a proactive attitude to make a difference and drive impact.
  • Demonstrates ownership over more complex projects and expertise in prioritising their own work.


#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.