Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior Data Scientist (12 Month FTC)

DEPOP
London
4 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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.