Data Scientist

Uncapped
London
1 month ago
Create job alert

A hybrid role based in London

Role Overview

The Data Scientist role will be responsible for designing, developing, and implementing advanced AI and machine learning models, utilizing both traditional and emerging approaches to address complex business challenges. This position is well-suited for with a PhD in Mathematics, Physics, Statistics, AI or equivalent, with a passion for solving real-world problems and hands-on experience of using both traditional ML and cutting-edge Large Language Models in business settings. This is a high-impact role that blends hands-on model development with practical leadership in scaling ML/AI systems across the business.

This position reports to the Chief Risk Officer and collaborates closely with engineering, product, and risk teams to ensure robust and impactful solutions.

About Uncapped

Founded in 2019, Uncapped is a fintech company focused on providing working capital to SMEs in North America and Europe.

We leverage multiple data sources to make credit decisions faster, safer and more conveniently. We are working with the largest platforms in the world, including Amazon and Walmart, and strive to be the best alternative-lender globally.

What you will do ️

Model Development: Apply advanced machine learning and statistical methods to develop and implement models across diverse use cases, including credit, commercial, product, and operations, with a significant emphasis on credit risk. ML Ops Leadership: Define and execute an ML Ops framework to streamline model lifecycle management, including data ingestion, data transformation, model training, deployment, and monitoring. Collaborative Problem Solving: Work with commercial and product teams to align ML solutions with business goals, ensuring risk considerations are integrated into new products and customer segments. Performance Tracking: Continuously monitor model performance and develop strategies to enhance accuracy and relevance, incorporating lessons learned into future iterations.

Requirements

Who you are

Educational Background: PhD in Mathematics, Physics, Statistics, AI or other related subjects. Deep Experience: 5+ years of experience in data science, including hands-on ML model development and production deployment. ML & Statistical Expertise: Expert in designing, training and tuning predictive models using both established statistical methods and modern AI techniques, with practical experience in deploying models to solve complex, real-world problems. High-growth Experience: Prior experience working in high-growth environments, ideally start-ups or scale-ups Coding Skills: Proficient in Python, SQL, and one of Pytorch or Tensorflow, with experience in writing, debugging, and optimising code. ML Ops Knowledge: Familiarity with tools like MLflow, Kubeflow or Vertex AI, and experience implementing CI/CD pipelines for machine learning. Understanding of Financial Services: Financial Services understanding is a plus, ideally in a lending environment. Strong Communicator: Can engage both technical and non-technical stakeholders

Benefits

What we offer

At Uncapped, our people make us successful. We are a start-up with big goals, and we work hard, so we want to give everyone the benefits they really want. We are continually adding to this list as new people join -- here are some of the things you can expect:

Unlimited holiday: we believe that well-rested and happy people make the best employees Competitive compensation plan Personal growth fund: Raise your game from great to spectacular Monthly recognition and awards: Celebrate wins big and small The opportunity to make a big impact every day on the lives of European and US entrepreneurs. Workspaces in Warsaw, London and Atlanta

We can only consider applications from candidates who are eligible to work in the UK without requiring visa sponsorship.

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist (NLP & LLM Specialist)

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.