Senior Data Scientist

Nottingham
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Junior / Graduate Data Scientist

Lead Data Scientist

Lead Data & AI Scientist

Lead Data & AI Scientist

Senior Machine Learning Engineer

Senior Data Scientist - Financial Services

  • Based in Nottingham (4 days onsite)

  • Permanent, Full-Time

  • Salary: up to £75,000 (depending on experience)

    A fast-growing financial services organisation is seeking a Senior Data Scientist to help drive innovation across the customer lifecycle. This is a hands-on, commercially focused role where you'll build and improve predictive models, support autonomous decisioning frameworks, and deliver actionable insights across fraud, marketing, credit risk, and customer management.

    You'll join a collaborative team and work closely with cross-functional squads, contributing to impactful projects while developing your skills in machine learning, experimentation, and modern data tooling.

    Key Responsibilities:

    Model development & improvement: Build, validate and maintain predictive models (e.g., credit risk, fraud, marketing response, collections) with guidance from senior teammates.
    Decisioning support: Translate models into business decisions through clear documentation, model outputs, and policy/testing setups.
    Experimentation: Design and analyse A/B and champion-challenger tests; deliver insights with clear visuals and concise narratives.
    Data exploration & analysis: Perform exploratory analyses to identify opportunities and support business roadmaps.
    Collaboration & learning: Work in cross-functional squads, share findings with technical and non-technical audiences, and grow your expertise in ML, AI, and GenAI tools.

    Key Skills and Experience:

    1-3 years of relevant experience delivering parts of the data science lifecycle.
    Proficiency in Python (or R), SQL, and experience with notebooks, Git workflows, and Power BI.
    Working knowledge of supervised machine learning (e.g., gradient boosting, logistic regression), evaluation metrics, and experiment design.
    Exposure to MLOps concepts, cloud platforms (e.g., Azure), and GenAI tools is a strong plus.
    Structured thinking, strong problem-solving, and clear communication skills.
    Degree (2:1 or equivalent) in a numerical discipline or relevant industry experience.

    Don't miss this opportunity to take a key role in shaping data-driven decisioning within a dynamic financial services organisation. Apply now with your most up-to-date CV!

    Please be aware this advert will remain open until the vacancy has been filled. Interviews will take place throughout this period, therefore we encourage you to apply early to avoid disappointment.

    Tate is acting as an Employment Business in relation to this vacancy.

    Tate is committed to promoting equal opportunities. To ensure that every candidate has the best experience with us, we encourage you to let us know if there are any adjustments we can make during the application or interview process. Your comfort and accessibility are our priority, and we are here to support you every step of the way. Additionally, we value and respect your individuality, and we invite you to share your preferred pronouns in your application

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.

Careers

How Many Robotics Tools Do You Need to Know to Get a Robotics Job?

If you’re pursuing a career in robotics, it can feel like the list of tools you should learn never ends. One job advert asks for ROS, another mentions Gazebo, another wants experience with Python, Linux, C++, RobotStudio, MATLAB/Simulink, perception stacks, control frameworks, real-time OS, vision libraries — and that’s just scratching the surface. With so many frameworks, languages and platforms, it’s no wonder robotics job seekers feel overwhelmed. But here’s the honest truth most recruiters won’t say explicitly: 👉 They don’t hire you because you know every tool — they hire you because you can apply the right tools to solve real robotics problems reliably and explain your reasoning clearly. Tools matter — but only in service of outcomes. So the real question isn’t how many tools you should know, but which tools you should master and why. For most robotics roles, the answer is significantly fewer — and far more focused — than you might assume. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable, confident, and ready to contribute from day one.

Jobs

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

Robotics is one of the most dynamic, interdisciplinary fields in technology — blending mechanical systems, embedded software, controls, perception (AI/vision), modelling, simulation and systems integration. Hiring managers in this space are highly selective because robotics teams need people who can solve real-world problems under constraints, work across disciplines, and deliver safe, reliable systems. And here’s the reality: hiring managers do not read every word of your CV. Like in many tech domains, they scan quickly — often forming a judgement in the first 10–20 seconds. In robotics, those first signals are especially important because the work is complex and there’s a wide range of candidate backgrounds. This guide unpacks exactly what hiring managers look for first in robotics applications and how to optimise your CV, portfolio and cover letter so you stand out in the UK market.

Education

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

Robotics is no longer confined to science fiction or isolated research labs. Today, robots perform critical tasks across manufacturing, healthcare, logistics, agriculture, defence, hospitality and even education. In the UK, businesses are embracing automation to improve productivity, reduce costs and tackle labour shortages. Yet despite strong interest and a growing number of university programmes in robotics, many employers report a persistent problem: graduates are not job-ready for real-world robotics roles. This is not a question of intelligence or dedication. It is a widening skills gap between what universities teach and what employers actually need in robotics jobs. In this article, we’ll explore that gap in depth — what universities do well, where their programmes often fall short, why the disconnect exists, what employers really want, and how you can bridge the divide to build a thriving career in robotics.

🍪 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.