Data Science Manager

Harnham
Glasgow
8 months ago
Applications closed

Related Jobs

View all jobs

Data Science Lead / Manager

Data science programme lead

Data science programme lead

Data Science and Innovation Manager

Manager, Data Science - Shipping

Sr Product Manager, Data Science

Data Science Manager

Remote (UK-Based)

Up to £75,000


The Company

This UK-based start-up has achieved rapid growth in just two years, now boasting a team of ~40 people across divisions. Following a successful funding round and with a strong pipeline ahead, they continue to scale at pace.

They specialise in predictive analytics and KPI tracking across a broad range of companies and industries. Their predictive insights empower hedge funds and investors with critical performance data, ahead of public earnings reports.


The Role

As a Data Science Manager, you’ll take ownership of the end-to-end development of KPI prediction models and manage a team of data scientists, helping refine their workflows and ensure high-quality deliverables.

You will:

  • Lead and mentor a team of data scientists in building predictive models.
  • Oversee data cleaning, feature engineering, and model development pipelines.
  • Build and maintain robust, scalable linear regression and statistical models for KPI forecasting.
  • Drive improvements in internal tooling and API integrations.
  • Collaborate closely with leadership, engineering, and the revenue team to translate business needs into data science solutions.
  • Play a key role in product innovation, helping shape how new data products are designed and delivered.


What They're Looking For

  • 5+ years’ experience in data science or a closely related field.
  • Proven leadership experience — mentoring or managing junior data scientists.
  • Expert Python programming skills (essential).
  • Strong grasp of linear regression, statistical modeling, and data processing best practices.
  • Proficient in SQL (MySQL preferred).
  • Experience with web scraping, machine learning techniques, and dashboarding tools is a bonus.
  • Familiarity with Docker, time series forecasting, or LLM technologies is advantageous.
  • A background or exposure to finance is useful but not mandatory.
  • Bachelor’s degree (or higher) in a quantitative or technical field.
  • Strong coding samples (e.g., GitHub projects).
  • Practical experience building production-level models and data pipelines.
  • Ability to bridge data science and product development goals.


If this role looks it could be of interest, please reach out to Joseph Gregory, or apply here.

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.