Senior Data Scientist/Engineer

Areti Group | B Corp
London
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist Research Engineer

Senior Data Scientist - Payments

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Science Engineer, Sports Modelling

Areti is seeking Senior Data Scientists/Data Engineers (Palantir, Python, Data Science) to work for a series A Funded tech start-up business based in the London area a very quick start.
YOU MUST BE DV OR SC CLEARED

You will be working on a number of large Defence and Government projects and be given the opportunity to lead a number of high-profile programs.

The Data Scientists will apply their technical skillsets and knowledge to solve exciting real-world problems in a small and dynamic company.

You will work as part of a dynamic and close-knit team building new and proprietary approaches to solving multiple problems across the industry. Experience in working as a Data Scientist/Data Engineer
Experience of completing projects to deadline.
Deploying ML models
Good command of Python and use of libraries for data science scikit-learn, NumPy, matplotlib
Relation database experience with data manipulation skills in SQL and large Big Data environments.
Command knowledge in Python and API Development
Excellent grasp of software Engineering practices Object Orientated Programming, GIT, AWS)
NoSQL Databases

Candidates must have security clearance - Ideally DV


My client has just received a huge investment and is now in the position where they are looking to scale and expand their Data team.

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.