2027 - Internship, Business Data Analytics

2027 –

Internship, Business Data Analytics

Programmes offered:

Final-year internship, penultimate-year internship

Location:

London

Programme duration:

3-6 months internship, starting in 2027

Who qualifies:

Penultimate or final-year students completing a bachelor’s or master’s degree

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager operating across liquid asset classes and markets worldwide. Our approach to investing is scientific: we bring together data, research, technology and trading expertise to develop and run systematic strategies.

Technology is central to how QRT works. Over the years, we have built a global research and execution platform spanning geographies, asset classes and trading horizons, from ultra-low-latency systems to large-scale research, analytics and risk infrastructure. We invest heavily in engineering, automation and scalable systems because better technology allows us to test ideas faster, operate more reliably and tackle harder problems.

Our internships are designed to give students meaningful experience of that work.

You’ll

join a team, contribute to real

projects and learn from engineers, researchers and traders working on live problems. The environment is technical, collaborative and demanding, with plenty of scope to ask questions, test ideas and take ownership.

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

Your future role at QRT

Join QRT’s growing Business Data & Analytics (BDA) team to turn internal and external data into practical business solutions. Work with developers and business stakeholders to research questions, explore complex datasets, and build tools that inform senior stakeholders.

We are recruiting across three internship tracks, depending on your strengths and interests:

Applied AI Engineering —

Build and evaluate tools using LLMs, agents and retrieval to support research, automate workflows and enhance applications.

Applied Data Science —

Structure and connect data, apply statistical, machine learning and graph-based methods, and build data applications for the business.

Investment Analytics —

Work with the investment team to turn complex data into clear insights and develop tools supporting investment decisions.

Your responsibilities will include:

• Translate business questions into analytical approaches, research and evaluate methods, and develop prototypes for integration with developers.
• Combine, structure and validate datasets across firms, teams and people; collect and analyse financial, market and portfolio data.
• Build dashboards monitoring investment performance, portfolio exposures, risk metrics, and market trends.
• Support investment research through quantitative analysis; communicate findings through clear visualisations and explanations for non-technical stakeholders.
• Assess accuracy and reliability, documenting assumptions and limitations.
• Prototype and test AI agents for document information extraction, research summaries, and recurring reports.
• Your present skillset
• Studying towards, or recently completed, a degree in Computer Science, Statistics, Data Science, Mathematics or another STEM discipline with relevant practical experience.
• Strong Python skills, clean, well-structured code, and experience cleaning, joining and modelling data from multiple sources.
• Intellectual curiosity about business, data and technology; strong analytical and problem-solving skills and a consulting mindset.
• Evidence of taking an open-ended problem from idea to working solution through projects, coursework, research or internships.
• Clear communication, initiative, and organisation, with confidence working through undefined requirements.
• Track-specific skills
• Demonstrate experience in your preferred track. Personal and passion projects are welcome: we are interested in what you built, your decisions, and what you learned.
• Applied AI Engineering —
• Hands-on experience with LLMs, including agents, tool use or retrieval; ability to assess AI outputs for accuracy, reliability and usefulness; interest in software deployment, operation and integration.
• Applied Data Science —

Experience applying analytical or statistical methods, visualising data and presenting findings to non-technical users. Prior machine learning experience is not required; curiosity and solid data experience are valued.

Investment Analytics —

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 collaboration.

Good to have

Graph data experience with Neo4j, NetworkX or similar.

SQL.

Cloud platforms, particularly AWS.

Machine learning, including community detection, link prediction and embeddings for the Data Science track.

Data applications or APIs using Dash or FastAPI.

Interview Process:

Application

– Submit your application online. We review applications on a rolling basis, so applying early is recommended.

Technical Assessment

– Selected candidates will complete a technical interview with the relevant team.

Interviews

– Shortlisted candidates will take part in interviews, either on-site or via Microsoft Teams. We’ll explore your technical foundations, how you reason through engineering problems and how you work with others.