2027 - Internship, Business Data Analytics

About the role

QRT’s London Business Data Analytics internship offers penultimate- and final-year students a 3–6 month opportunity starting in 2027. Interns will transform business, financial and investment data into practical solutions through applied AI engineering, data science or investment analytics, collaborating with developers, stakeholders, researchers and traders.

Programme and Responsibilities

• Translate business questions into analytical approaches, research methods and prototypes for developer integration.
• Combine, structure and validate datasets across firms, teams and people using financial, market and portfolio data.
• Build dashboards monitoring investment performance, portfolio exposures, risk metrics and market trends.
• Support quantitative investment research by communicating findings through visualisations and clear explanations.
• Assess data and model accuracy, reliability, assumptions and limitations; test AI agents for recurring reports and research summaries.

Internship Tracks

• Applied AI Engineering: Build and evaluate LLM, agent and retrieval tools supporting research, workflows and applications.
• Applied Data Science: Apply statistical, machine learning and graph-based methods to build business data applications.
• Investment Analytics: Develop tools and insights from complex data supporting investment decisions and investment-team research.

Eligibility and Core Skills

• Penultimate- or final-year bachelor’s or master’s students in Computer Science, Statistics, Data Science, Mathematics or another STEM discipline.
• Strong Python skills, including clean, structured code and experience cleaning, joining and modelling data sources.
• Strong analytical, problem-solving and consulting skills, with intellectual curiosity about business, data and technology.
• Evidence of taking open-ended problems from ideas to working solutions through projects, coursework, research or internships.
• Clear communication, initiative and organisation, including confidence working through undefined requirements.

Additional Skills

• Preferred track experience may include LLMs, statistical analysis, data visualisation, financial markets, investing, Python, Excel or automation.
• Useful additional experience includes graph data, SQL, AWS, machine learning, Dash, FastAPI or data applications and APIs.
• Personal and passion projects are welcome; candidates should explain what they built, their decisions and what they learned.

Application and Interviews

• Submit an online application; applications are reviewed on a rolling basis, and early application is recommended.
• Selected candidates complete a technical interview with the relevant team.
• Shortlisted candidates attend on-site or Microsoft Teams interviews covering technical foundations, engineering reasoning and collaboration.