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

Apply Now

Data Science Manager

Harnham
London
2 weeks ago
Applications closed

Related Jobs

View all jobs

Data Science Manager

Data Science Manager

Data Science Manager

Data Science Manager

Data Science Manager

Data Science Manager

Data Science Manager

Up to £85,000



Join a fintech platform revolutionising workplace savings and pensions by making sustainable investing simple for both employees and employers. They’re right where finance, tech, and wellbeing meet with backing from a major bank but the feel of fintech innovation.



Responsibilities:

  • Stay hands-on while advising business leaders on data-driven decisions.
  • Drive improvements in client and customer outcomes through active data analysis and modeling.
  • Develop and refine predictive and machine learning models.
  • Build and optimize customer lifetime value and key metric models.
  • Continuously improve analytics and model accuracy.
  • Lead and contribute to scalable model deployment in production.



Requirements:

  • MSc in Computer Science, Artificial Intelligence, Mathematics, Statistics or related fields.
  • Experience in Consumer / Marketing Analytics
  • Strong proficiency in Python and experience with relevant ML libraries/frameworks.



Please note:

This role requires 1 day onsite per week onsite in Central London.

This role cannot provide visa sponsorship.

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

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.