Hybrid 13-Month Data Science & Fraud Analytics Placement

LexisNexis Risk Solutions; Greater London; Hybrid; GBP 13,000 - 20,000; Full time

Benefits offered by this job

25 days annual leave

Graduate and placement networking

Volunteer and community engagement

Wellbeing and mental health support

Job summary

LexisNexis Risk Solutions in London offers a 13-month undergraduate placement in Data Science & Fraud Analytics, starting July 2027. This paid internship provides real‑world experience within our analytics teams, tackling fraud detection, risk analytics and digital identity challenges.

You’ll work with data scientists and consultants, build data pipelines and dashboards using Snowflake, dbt and Streamlit, and present insights to technical and non‑technical audiences across financial services,

Qualifications

• Studying a STEM-related degree (Data Science, CS, Maths, Statistics, Physics, Engineering or similar).
• Right to work in the UK for duration of placement.
• Interest in fraud, risk or cybercrime and data analytics.

Responsibilities

• Analyse large fraud and customer data to extract actionable insights.
• Build data pipelines using Snowflake and dbt.
• Develop interactive dashboards with Streamlit and visualisations.
• Apply ML techniques to fraud detection and risk management problems.
• Present findings to technical and non-technical audiences.
• Collaborate with data scientists, analysts and consultants on customer projects.

Skills

Python

SQL

Snowflake

dbt

Streamlit

Machine learning frameworks

Generative AI tools

Copilot and AI agents

Education

STEM-related degree

Tools

Snowflake

dbt

Streamlit

Job description

LexisNexis Risk Solutions in London offers a 13-month undergraduate placement in Data Science & Fraud Analytics, starting July 2027. This paid internship provides real‑world experience within our analytics teams, tackling fraud detection, risk analytics and digital identity challenges.

You’ll work with data scientists and consultants, build data pipelines and dashboards using Snowflake, dbt and Streamlit, and present insights to technical and non‑technical audiences across financial services,