Data Science & AI Internship 2026

About IK Partners

IK Partners (“IK”) is a European private equity firm focused on investments in the Benelux, DACH, France, Nordics and the UK. Since 1989, IK has raised more than €20 billion of capital and invested in over 200 European companies. IK supports companies with strong underlying potential, partnering with management teams and investors to create robust, well-positioned businesses with excellent long-term prospects. For more information, visit ikpartners.com

IK's prime target markets include the Nordics, Benelux, France, DACH and more recently the UK. Headquartered in London, IK has additional offices in France, Germany, Denmark, the Netherlands and Sweden for its dedicated regional investment teams, consisting of over 90 investment professionals.

IK is an affiliate of Wendel. For more information, visit wendelgroup.com

IK is looking for its new Data Science & AI Intern to support them during a full-time/part-time internship of 5 months from November 2026. The role will be office-based. 

The Role

You will work directly with IK's Lead of Data Science & AI in the Operations team, taking on the data and analytical work that sits behind the AI programmes running across IK's portfolio companies. The role is analysis-led. Most of the time will be spent working with data to answer a specific question, with a smaller share on desk research to find and assess data sources. Finished work is delivered as a small set of slides: the analysis, the finding, and a view on what it means. Work is set out in written specs with a defined input and output, and the intern is then given the time to deliver against them. The placement is internal and office-based, and findings are presented back to the Lead of Data Science & AI rather than to portfolio companies or deal teams, so there is no requirement to present externally.

Key Responsibilities

• Analysing operational data from portfolio companies to answer a specific question, for example working out where cost or time actually sits in a process.

• Building ad-hoc bounded solutions, like an AI-powered pipeline to digitise scans of text into digital text on a large scale. 

• Finding and evaluating external data sources that can be brought in to strengthen an analysis, and forming a view on how reliable and usable each one is.

• Turning each piece of work into a short set of slides with clear charts and a clear message, and walking through it with the Lead of Data Science & AI.