About the job
A dynamic hybrid role based in London
Role Overview
As a Data Scientist at Uncapped, you will take the lead in designing, developing, and implementing sophisticated AI and machine learning models. Your expertise will be crucial in addressing complex business challenges through both traditional and innovative approaches. This role is ideal for candidates holding a PhD in Mathematics, Physics, Statistics, AI, or a related field, who possess a genuine passion for solving real-world problems. You should also have substantial hands-on experience with both traditional machine learning techniques and cutting-edge Large Language Models in practical business applications. This impactful position combines model development with leadership in scaling ML/AI systems across the company.
Reporting directly to the Chief Risk Officer, you will collaborate closely with engineering, product, and risk teams to ensure the delivery of robust and impactful solutions.
About Uncapped
Founded in 2019, Uncapped is a pioneering fintech firm dedicated to providing working capital to small and medium-sized enterprises (SMEs) throughout North America and Europe.
By leveraging multiple data sources, we expedite credit decisions, making them faster, safer, and more convenient. Our partnerships with leading platforms such as Amazon and Walmart underline our commitment to becoming the foremost alternative lender globally.
What You Will Do
- Model Development: Utilize advanced machine learning and statistical techniques to develop and deploy models across various applications, with a primary focus on credit risk, commercial insights, product development, and operational efficiency.
- ML Ops Leadership: Spearhead the definition and execution of an ML Ops framework, optimizing model lifecycle management, including data ingestion, transformation, training, deployment, and monitoring.
- Collaborative Problem Solving: Partner with commercial and product teams to ensure ML solutions align with business objectives while integrating risk considerations into new offerings and customer segments.
- Performance Tracking: Vigilantly monitor model performance, devising strategies for accuracy enhancement and relevance, while incorporating feedback for continuous improvement.
