Principle Data Scientist

Dabster
Staines-upon-Thames
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist - Experimentation & Ads Analytics

Data Scientist - Experimentation & Ads Analytics

Senior Data Scientist

Lead Data Scientist - Customer Development

Senior Data Scientist

Principal Data Scientist

Note:

Candidate Experience level + YearsExperience Should be in Data, Not in Computer vision(Image)


JD:

Join our team to redefine the landscape of financial services through the lens of Risk Intelligence Innovation. We're looking for a Data Scientist who is ready to dive into the dynamic fields of digital identity, and fraud. Your role will be crucial in shaping the future of these areas.

We are searching for a candidate who is not only passionate about risk intelligence but also brings a wealth of experience in managing complex, large-scale projects. Your role will involve collaboration with both internal and external stakeholders, demanding a high level of proficiency in communication and teamwork.

You will report directly to the Director of Data Science and play a key role in guiding the development of Client and AI technology within the organization.

Your responsibilities will include the analysis of complex datasets, designing algorithms and models to detect and prevent financial crimes, and contributing to the design of Client, AI and Data infrastructure in line with Risk Intelligence' objectives.

Your expertise in Data Science and Client and AI related technologies will be pivotal in steering the direction of our development efforts.

This role is not just a job, but a journey into the future of risk intelligence, where your skills and insights will contribute significantly to our innovative endeavors.

About You– experience, education, skills, and accomplishments Advanced degree in Computer Science, Statistics, Technology, or Engineering, or equivalent work experience. Minimum years of industry experience with years of proven track record in the application of AI, Client, and NLP. Excellent programming skills (Python, Java, and R) Good communication & presentation skills: connecting people, gathering data & information across business unit boundaries, and telling & selling the story are no problem for you.
It would be great if you have. . .. Excellent understanding of Data Science Theory, LLMs, Client, NLP, and statistical methodologies in a data analytics environment. Ability to test ideas and adapt methods quickly end to end from data extraction to implementation and validation. Experience with search engines, web scraping, data classification algorithms, recommendation systems, and relevance evaluation methodologies
What will you be doing in this role?Researches and identifies Artificial Intelligence (AI), Large Language Models (LLMs) and Machine Learning (Client) methods and algorithms to solve specific problems within Risk Intelligence Implements these methods and devises appropriate test plans to validate and compare the different approaches. Identifies new applications of AI, LLMs, and Client in the context of our extensive sets of content and data. Explores existing data for insights and recommends additional sources of data for improvements.
About the teamThis is a new Data Science innovation team within the Risk Intelligence Engineering function. Our engineers use Machine Learning to solve problems along the entire Lifecycle of Innovation, with a strong focus on new capabilities and approaches to existing problems. Algorithms to detect fraud, predicting risk and outcomes are just a few of the ways our team is fostering productivity of our customers who lead innovation in the world. We are a global team reporting into the Head of Risk Intelligence in the US. The team culture is all about openness & collaboration with a focus on excellence and value adding innovation. We have a tradition of also being a source of innovation ourselves, based on deep domain knowledge, so being creative and entrepreneurial is very much encouraged.

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.