Data Scientist

CBSbutler Holdings Limited trading as CBSbutler
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist - Renewable Energy

Data Scientist - data and analytics

+6 months

+Fully remote working

+Inside IR35

+£450 - £525 a day

We're looking for an experienced Data Scientist to play a key role in turning complex data into clear, actionable insights. You'll be responsible for the full data lifecycle - from collection and cleaning to analysis, modelling, and communication of findings - ensuring all work aligns with project objectives and timelines. This is a highly collaborative role, working closely with our existing team to deliver high-quality results and meet project deadlines.

The role:

Collect, clean, and preprocess structured and unstructured data from multiple internal and external sources.
Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies.
Design and implement data pipelines for model-ready datasets in collaboration with data engineering teams.
Apply feature engineering and selection techniques to improve model accuracy and interpretability.
Develop and validate machine learning and statistical models for prediction, classification, clustering, or optimization.
Apply supervised and unsupervised learning techniques using libraries such as Scikit-learn, TensorFlow, or PyTorch.
Implement NLP, time-series forecasting, or optimization algorithms based on project requirements.
Evaluate models using appropriate metrics and perform hyperparameter tuning for optimal performance.
Convert proof-of-concept models into production-grade pipelines in collaboration with MLOps and engineering teams.Required:

Translate model outcomes into actionable insights through clear storytelling and visualizations.
Build dashboards and reports using Power BI, Tableau, or Python-based visualization tools.
Communicate findings to both technical and non-technical stakeholders effectively.
Partner with business analysts, architects, and domain experts to define use cases and success metrics.
Contribute to the enterprise AI roadmap, bringing thought leadership on analytical methodologies.
Document methodologies, model logic, and validation results for audit and reproducibility.
Participate in Agile ceremonies, sprint planning, and client showcases.If you'd like to discuss this data scientist role in more detail, please send your updated CV to (url removed) and I will get in touch

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.