Data Scientist

X4 Technology
UK
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist - Imaging - Remote - Outside IR35

Data Scientist - Measurement Specialist

Data Scientist (Predictive Modelling) – NHS

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Job Title: Data Scientist Location: London, Remote Contract Type: 3-6 months Outside IR35 Rate : £400-£450/day We are seeking an experienced and motivated Data Scientist to lead a critical project focused on machine learning models. You will work closely with our engineering, product, and business teams to gather insights, drive data-driven decisions, and build scalable solutions that directly impact our company’s strategy. The ideal candidate has a strong foundation in statistical analysis, machine learning, and data modelling, with the ability to communicate complex technical ideas to non-technical stakeholders. Data Scientist Key Skills Responsibilities: Data Collection & Preprocessing: Gather and clean large datasets from multiple sources (databases, APIs, etc.), ensuring data quality and integrity. Modeling & Analysis: Develop, test, and optimize predictive models (e.g., classification, regression) using advanced techniques in machine learning and statistics. Exploratory Data Analysis (EDA): Analyze data to uncover trends, patterns, and insights that can inform strategic business decisions. Collaboration: Work closely with cross-functional teams, including product managers, software engineers, and domain experts, to align the project’s data goals with business needs. Visualization & Reporting: Present findings in a clear, actionable format, using data visualization tools like Tableau, Power BI, or custom dashboards. Model Deployment: Implement and maintain machine learning models in production environments, ensuring scalability and performance. Project Management: Oversee the project timeline, deliverables, and ensure alignment with stakeholders. Qualifications: Education: Bachelor’s or Master’s in Data Science, Computer Science, Mathematics, Statistics, or related fields. Experience: 3 years of experience in data science, analytics, or machine learning projects. Skills: Proficiency in Python or R , and data manipulation libraries (e.g., Pandas, NumPy). Experience with machine learning frameworks like Scikit-learn , TensorFlow , or PyTorch . Strong knowledge of SQL and database management. Expertise in data visualization tools (e.g., Tableau , Power BI , Matplotlib ). Experience in cloud platforms like AWS , GCP , or Azure is a plus. Problem-Solving: Excellent analytical and problem-solving skills, with the ability to work through complex challenges. Communication: Strong verbal and written communication skills, particularly in explaining technical concepts to non-technical stakeholders. Please apply via the link if you think this position is a good fit, or reach out to me to review other roles that might match your expertise.

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.