Senior Data Scientist

Tate
Nottingham
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist - National Security (TIRE) based in Cheltenham/H

Senior Data Scientist and Machine Learning Researcher

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.


#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.

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

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

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

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.