2027 - Internship, Business Data Analytics

Description

Business Data Analytics internship, offered in two programmes: a final-year internship and a penultimate-year internship.

The company is a global quantitative and systematic investment manager trading liquid assets and markets worldwide. It takes a scientific approach, combining data, research, technology and trading expertise to build and run systematic strategies.

Technology is at the core of the business. The company runs a global research and execution platform across regions, asset classes and trading horizons, from ultra-low-latency systems to large-scale research, analytics and risk infrastructure. It invests heavily in engineering, automation and scalable systems so it can test ideas faster and run more reliably.

The Business Data & Analytics team turns internal and external data into practical business solutions. You will work with developers and business stakeholders on research questions, complex datasets and tools for senior decision makers.

Three tracks are available, so you can choose the one that fits your strengths. Applied AI Engineering: create and assess tools built on LLMs, agents and retrieval that aid research, cut manual workflow steps and strengthen applications. Applied Data Science: organise and link datasets, use statistical, machine learning and graph methods, and produce data applications for the business. Investment Analytics: partner with the investment group to make sense of complicated data and build tools that guide investment choices.

As an intern you join a team, work on live projects and learn from engineers, researchers and traders. The setting is technical, collaborative and demanding, with room to ask questions, test ideas and take ownership.

Responsibilities

• Convert business questions into analytical approaches, test and assess methods, and build prototypes that developers can integrate.
• Merge, organise and check datasets from different firms, teams and people, and gather financial, market and portfolio data for analysis.
• Create dashboards that track investment performance, portfolio exposures, risk measures and market trends.
• Carry out quantitative analysis for investment research and explain findings with clear charts and plain-language write-ups for non-technical audiences.
• Check accuracy and reliability, and record assumptions and limitations.
• Build and test AI agents that extract information from documents, summarise research and produce recurring reports.

Requirements

• Currently studying toward, or recently finished, a degree in Computer Science, Statistics, Data Science, Mathematics or another STEM field, with relevant practical experience.
• Solid Python skills: you write clean, well-structured code and have cleaned, joined and modelled data from several sources.
• Curiosity about business, data and technology, strong analytical and problem-solving skills, and a consulting mindset.
• Evidence of taking an open-ended problem to a working solution through projects, coursework, research or internships.
• Clear communication, initiative and organisation, and confidence working with requirements that are not yet defined.
• Show experience in your chosen track. Personal and passion projects are welcome; the company wants to know what you built, what you decided and what you learned.
• Applied AI Engineering: hands-on work with LLMs, including agents, tool use or retrieval; ability to judge AI output for accuracy, reliability and usefulness; interest in deploying, running and integrating software.
• Applied Data Science: experience applying analytical or statistical methods, visualising data and presenting results to non-technical users. Machine learning experience is not required; curiosity and solid data experience count.
• Investment Analytics: strong attention to detail, interest in financial markets and investing, working knowledge of Python and Excel for data analysis, and interest in LLMs, AI agents and automation; clear communication, initiative and teamwork.
• Good to have: graph data experience with Neo4j, NetworkX or similar.
• Good to have: SQL.
• Good to have: cloud platforms, particularly AWS.
• Good to have: machine learning, including community detection, link prediction and embeddings for the Data Science track.
• Good to have: data applications or APIs using Dash or FastAPI.

Conditions

The internship lasts 3-6 months and starts in 2027.

Open to penultimate-year or final-year students working toward a bachelor's or master's degree.

Interviews take place on-site or via Microsoft Teams.

Applications are reviewed on a rolling basis, so applying early is advised.

The process includes a technical assessment with the relevant team, followed by interviews covering technical foundations, how you reason through engineering problems and how you work with others.

Strong performance during the internship may lead to consideration for a full-time graduate role.

The company is an equal opportunity employer that values diversity.

Benefits

• The company runs initiatives and programmes to support a healthy work-life balance.