Undergraduate Placement - Data Science & Fraud Analytics
Core
13-month paid industrial placement for STEM students to work on fraud detection and risk analytics solutions for global clients in financial services, e-commerce, and digital platforms.
Role type
Undergraduate placement data scientist (fraud analytics)
Builds
Fraud detection strategies, data pipelines, and analytical applications for global customers
Domain
Financial services, e-commerce, digital platforms, fraud prevention
Deliverable
production ML models
Required skills
Python, SQL, Snowflake, dbt, Streamlit, machine learning, generative AI tools, data visualization
Preferred skills
Personal data science/AI projects, open-source contributions, hackathons, analytics portfolios, GitHub projects, experience with Snowflake/dbt/Streamlit/ML/AI tools, interest in fraud/cybersecurity/risk
Technologies
Snowflake, dbt, Streamlit, Copilot, AI agents, Python, SQL
Responsibilities
Analyse large-scale fraud and customer behaviour datasets, build and optimise data pipelines, develop interactive dashboards, apply machine learning techniques to fraud detection, support development of fraud detection strategies, present findings to technical and non-technical audiences
Seniority
Undergraduate placement (returning to university after)
