Data Scientist Contractor

London
11 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientists x 3

Data Scientist

Data Scientist

Data Scientist (Predictive Modelling) – NHS

Data Scientist - Measurement Specialist

Azure Data Scientist - Contract

💰 Rate: £600 per day (Outside IR35)
📍 Location: Central London (Hybrid - 1 day per week onsite)

About the Role:

We are seeking an experienced Azure Data Scientist to join a forward-thinking team on a contract basis. You'll be responsible for designing, developing, and deploying machine learning models and AI solutions using Azure's cloud ecosystem. This role is perfect for someone who thrives on solving complex data challenges and delivering insights that drive business decisions.

Key Responsibilities:

Design and implement machine learning models using Azure Machine Learning (AML) and related services.
Develop data pipelines and integrate models into production using Azure Databricks, Synapse, and Data Factory.
Work with large datasets, applying AI/ML techniques for predictive analytics and optimization.
Deploy and monitor machine learning models in production using MLOps best practices.
Collaborate with data engineers, analysts, and stakeholders to define data strategies and ensure business value.
Optimize data storage, processing, and retrieval using Azure Data Lake, SQL Server, and Cosmos DB.

Required Skills & Experience:

Strong experience in Data Science & Machine Learning with a proven track record in Azure environments.
Proficiency in Python, PySpark, and SQL for data manipulation and model development.
Hands-on experience with Azure Machine Learning (AML), Databricks, Synapse, and Cognitive Services.
Solid understanding of MLOps, CI/CD pipelines, and deployment automation.
Familiarity with Azure DevOps, Kubernetes (AKS), and containerization (Docker) is desirable.
Experience in Natural Language Processing (NLP), Computer Vision, or Deep Learning is a plus.
Strong problem-solving skills with the ability to work independently and in cross-functional teams

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.