Careers

We are seeking interns to join our team and play a key role in supporting the development and business processes. You’ll be working at the intersection of finance and technology, handling complex credit datasets that power our AI-driven analytics platform used by major European financial institutions. 

As part of a lean, agile team, you’ll have significant autonomy in your work while being supported by experienced colleagues. You’ll interact directly with business stakeholders, gaining valuable insight into how data drives real-world financial outcomes. This is an excellent opportunity for a hands-on technologist who is eager to take responsibility, work on impactful projects, and grow within a fintech environment. All internships are paid and have a minimum length of three months with some flexibility. Work is performed remotely with the option to come to the London (UK) office occasionally. 

Available Opportunities:

1. Data Processing and Reporting Pipelines

Project Type: Back end development for automated and semi-automated data processing including the use of AI tools

Students will support the development and management of our manual, semi-automated and fully automated data and reporting pipelines for loan portfolio and securitisation data. Students will work at the intersection of data engineering, analytics, and AI-driven automation, contributing to backend development projects. Students should have experience in Python or R and should be familiar with AI coding tools and GitHub. This internship is suitable for students in any quantitative field with strong coding skills and an interest in finance.

2. AI Agent Testing and Evaluation Framework

Project Type: Enhance the test and evals framework in AI agents used in data analysis, data mapping and validation.

Students will support the ongoing development of Accuria’s AI Data Agent by expanding and implementing tests and evals frameworks to improve quality and replicability of agentic workflows. Students will work at the intersection of data engineering, analytics, and AI-driven automation, contributing both to backend and full-stack development projects. Students should have experience in Python and should be familiar with AI coding tools and agentic AI frameworks. This internship is suitable for students in computer science or other quantitative fields with strong coding skills and a strong interest in finance and AI.

3. GenAI Credit and Market Research Tool

Project Type: Generative AI processing and validation pipeline

As an international marketplace connecting buyers and sellers, Accuria constantly improves its client database coverage of potential buyers and sellers of performing and non-performing loans. Our investor database includes information like jurisdictions, asset classes, return targets, recent investment activities, available capital and any other detail investors want potential sellers to know to be invited into the competitive sales processes run by Accuria. In this project, we aim to improve our understanding of our investors by upgrading the investor profiles we maintain in our database. Interns are expected to create additional transaction and investor profiles with the help of AI search tools and deep research. Interns are required to have prior programming experience in either Python or R and good knowledge of using LLMs and AI tools for code development. This internship is suitable for students in computer science, finance, economics, and business with strong coding skills and an interest in finance.

4. Country-Level DataHub Pipelines and Analytics

Project Type: Data engineering, automation, and analytics

The Country-level DataHub is an analytics product that aggregates and standardises macroeconomic, credit, and market data across jurisdictions to support country-level risk analysis and monitoring for loan portfolios and securitisations. Students will support the maintenance and further development of the data pipelines feeding the DataHub, including building and improving automated workflows and data pipelines (e.g. using Argo), integrating FX and interest-rate API feeds, and contributing to frontend dashboards and data visualisation, gaining exposure across the full data stack. Students should have experience in R and be familiar with APIs, data pipelines, and GitHub; exposure to frontend technologies or data visualisation tools is a plus. This internship is suitable for students in computer science, economics, finance, or other quantitative fields with an interest in developing coding skills and financial analytics.

5. NPL Valuation Data Set Maintenance & Curve Extraction

Project Type: Machine learning models to forecast NPL cashflows.

Building on our earlier internal and externally published research, students will update the reference database and update existing model and recovery curve extraction processes. Students will support the development of valuation functions to help value granular heterogeneous non-performing and subperforming loans. Interns are required to have prior programming experience in either Python or R, experience with ML model selection and validation, and a good knowledge of using LLMs and AI tools for code development.

6. Data Lakehouse Setup

Project Type: Prototyping a cloud-native analytics data lakehouse

Students will support the design and implementation of a prototype data lakehouse that will form the foundation of how our organisation stores, manages, and analyses its core analytics datasets going forward. The project will involve researching and evaluating open-source lakehouse technologies (such as Apache Iceberg, Delta Lake, DuckDB/DuckLake) and conducting a viability assessment against our existing data infrastructure and required use cases, with guidance and support from the team. Based on an agreed proposal, students will then be tasked with developing a production-ready setup and migrating existing analytics datasets. Where time and capability allow, students may also contribute to integrating the new lakehouse architecture into our broader analytic platform. This internship is open to students from any discipline who can demonstrate strong technical skills and an interest in building modern data platforms. Applicants should be comfortable programming in languages used for data engineering and analytics (e.g. Python, SQL, R, or similar), understand how analytical datasets are stored and processed, and have experience using Git-based workflows. Familiarity with cloud-hosted data systems or deploying services in a cloud environment is a bonus.

7. AI Agent for Loan Due Diligence and Payment Account Reconciliations

Project Type: Improving an agentic AI process to conduct financial due diligence on documents provided during due diligence of loan sales.

In this project, the intern will be given access to Accuria’s AI agent for data and document analysis and will be asked to contribute to problems covering document classification, OCR, data extraction, data templates and automatic validation, document and data tape validation with human in the loop quality checks. Students must have worked in Python on complex data and document analysis projects, must be familiar with AI coding tools, and preferably have worked with one or more agentic framework before. This internship is suitable for students in computer science or econometrics with strong coding skills and an interest in AI.

8. Predictive Analytics of Optimal Loan Restructuring

Project Type: Algorithmic optimisation of payment plans and restructured loans for borrowers in distress.

Accuria values non-performing loans using discounted cash flow analysis. Private individuals and companies often have the capacity to make some regular payments which differ from the original loan payment schedule. This project is about designing and implementing a production ready algorithm which determines the regular payment amount while meeting a number of boundary conditions and optimisation criteria (maximising net present value for the lender or targeting a minimum NPV, meeting affordability criteria etc, minimising the chance of a second default etc). Candidates must have coding experience in Python or R and must be familiar with AI coding tools. Familiarity with optimisation problems and discounted cash flow analysis is an advantage. This internship is suitable for students in economics, econometrics or finance with strong coding skills.

9. Predictive Analytics and Cash Flow Forecast for Commercial Real Estate Loans and CMBS

Project Type: Create a structural (bottom up) credit risk model and cash flow forecasting tool for commercial real estate loans.

Based on existing forecast and valuation tools from Accuria, students are expected to improve and extend the financial models for commercial real estate loans. Interns will be given real loan data for construction loans and income generating loans with the view to forecast construction progress and tenant cash flows under different stress scenarios to improve existing rating, valuation and stress testing models. Models work at the tenant, property, borrower, loan and securitisation level. Students should have experience in Python or R and should be familiar with AI coding tools. A familiarity with credit risk models, other financial models, and discounted cash flow analysis is an advantage. This internship is suitable for students in economics, econometrics or finance with strong coding skills.

10. Cash Flow Forecasts and Optimisation for Revolving Securitisation Transactions

Project Type: Create an algorithm to model newly originated loans and their optimal selection for the funding through revolving securitisation transactions.

Based on existing forecast and valuation tools from Accuria, students are expected to improve and extend the financial models to manage the loan production pipelines of banks, debt funds and non-bank lenders who use securitisation as a funding or risk transfer tool. Interns will be given real loan data and securitisation details with the objective to generate hypothetical future origination flows and test those flows for inclusion in the securitisation under various performance scenarios. Students should have experience in Python or R and should be familiar with AI coding tools. A familiarity with credit risk models and securitisations structures is an advantage. This internship is suitable for students in economics, econometrics or finance with strong coding skills.

11. AI-Driven Marketing Automation & Campaign Development

Project Type: Marketing & AI Automation

Students will play a hands-on role in both building AI-powered marketing tools and executing real marketing campaigns. On the technical side, they’ll help design and develop automated workflows using Generative AI to streamline the creation of marketing content from Accuria’s articles and product releases. On the marketing side, they’ll contribute to the planning and preparation of traditional marketing campaigns, including content creation, campaign scheduling, and promotional material for upcoming product launches.

This internship is ideal for students from any field who are passionate about marketing, finance, and technology with an interest and knowledge of Generative AI, and have coding experience in Python or R. Experience with WordPress or digital marketing platforms is a plus.

Rewards & Benefits

• Competitive compensation depending on experience and location
• Start dates are flexible until H2 2026
• High-performing interns may be offered a full-time contract upon completion of their internship
• Remote-first working arrangement with the option to come to the London office occasionally

How to Apply

Send your CV and a short cover letter, indicating which opportunities interest you, to careers@accuria.com.