Data Science - London - £75,000 - £85,000 + Benefits

City of London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Science Practitioner

Data science programme lead

Data science programme lead

Data Science Practitioner

Data science programme lead

Data Science Practitioner

Data Science - London - £70,000 - £85,000 + Benefits

My client is sitting on billions in an investment fund and itching to invest - this is where you come in. The deal team will come to you with a potential investment opportunity and it is up to you to perform due diligence in order to assess the validity and risk level of the investment opportunity.

My client are a transnational investment management company who currently manage close to 20 billion in investments and are constantly growing. Within the company you have the opportunity to work with some fascinating data opportunities that tie into their investments.

The role offers an already competitive salary but also boasts great benefits - including a potential 12.5% bonus which 90% of employees meet, and some exceed!

Requirments:

NLP
Python
Some LLM
Some CI/CD
Good Communication
Commitment to 4 days in London office Desirable:

Investment / Financial Services Experience

Interviews commence shortly with limited availability, don't miss out on your opportunity to secure this amazing role. Get in touch ASAP by contacting me at or on (phone number removed)!

Data Science, Data Scientist, LLM, CI/CD, NLP, Innovative Solutions, Data-Driven, Machine Learning, Big Data, Predictive Analytics, AI Integration, Collaborative Environment, Impactful Insights, Scalable Solutions, Continuous Learning, Investment, Banking, Trading

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.