Undergraduate Placement - Data Science & Fraud Analytics

Nextens; Greater London; Hybrid; GBP 20,000 - 24,000; Full time

Benefits offered by this job

Hybrid working

25 days annual leave (option to buy)

Graduate and placement networking

Volunteer and community engagement

Wellbeing and mental health support

Job summary

LexisNexis Risk Solutions is offering a 13‑month Undergraduate Placement in Data Science & Fraud Analytics in London (Hybrid), starting July 2027 with the application closing in January 2027. You’ll work across Professional Services and Data Science to deliver real‑world fraud analytics projects in financial services, e‑commerce and digital platforms.

You’ll gain hands‑on experience delivering measurable business impact, collaborating with data scientists, fraud analysts, data engineers and

Qualifications

• Studying a STEM‑related degree (Data Science, CS, Math, Statistics, Physics, Engineering).
• Returning to university after the placement year.
• Right to work in the UK for the duration of the placement.

Responsibilities

• Analyse large‑scale fraud and customer behaviour datasets to uncover actionable insights.
• Build and optimise data pipelines using Snowflake and dbt.
• Develop interactive dashboards and analytical apps using Streamlit.
• Explore and apply ML techniques to fraud detection and risk management.
• Use AI tools to accelerate analysis and automate workflows.
• Support development of fraud detection strategies for global customers.
• Collaborate with consultants and data scientists to solve customer challenges.
• Present findings to technical and non‑technical audiences.
• Contribute to innovation projects shaping fraud prevention and digital identity.

Skills

Python

SQL

Snowflake

dbt

Streamlit

Machine learning

Generative AI

Copilot & AI agents

Data visualization

Education

STEM degree (Data Science/CS/Math/Engineering)

Tools

Snowflake

dbt

Streamlit

Job description

Undergraduate Placement Data Science & Fraud Analytics

APPLICATION CLOSING DATE January 2027

START DATE July 2027

LOCATION London (Hybrid)

DURATION 13 months

Ready to take on real-world challenges?

Are you looking for a paid industrial placement as part of your degree and want to work on problems that genuinely matter?

At LexisNexis Risk Solutions, you’ll join an analytics team that helps some of the world’s largest organisations combat fraud, reduce risk and make smarter decisions through advanced analytics, machine learning and digital identity intelligence.

We’re looking for curious, ambitious students who enjoy tackling complex problems, working with data and learning new technologies. If you thrive on challenges and want to apply your skills to real customer problems at scale, we’d love to hear from you.

The Placement:

This 13-month placement offers an opportunity to work within our Professional Services and Data Science teams, supporting fraud detection and risk analytics solutions used by global organisations across financial services, e-commerce, and digital platforms.

Rather than working on theoretical projects, you’ll gain hands‑on experience delivering solutions that have a measurable business impact. You’ll work alongside data scientists, fraud analysts, data engineers and consultants to develop insights, optimise products and help customers stay ahead of increasingly sophisticated fraud threats.

You will have access to modern cloud-based technologies and AI‑assisted tools, working with large‑scale datasets and real‑world fraud challenges from day one.

What You’ll Be Doing:

• Analyse large-scale fraud and customer behaviour datasets to uncover actionable insights.
• Build and optimise data pipelines using modern analytics platforms such as Snowflake and dbt.
• Develop interactive dashboards and analytical applications using Streamlit.
• Explore and apply machine learning techniques to fraud detection and risk management problems.
• Use Copilot and AI‑powered agents to accelerate analysis, automate workflows and improve productivity.
• Support the development of fraud detection strategies for global customers.
• Work with consultants and data scientists to solve complex customer challenges.
• Present findings and recommendations to both technical and non‑technical audiences.
• Contribute to innovation projects that shape the future of fraud prevention and digital identity.

We’re Looking For Students Who:

Love Solving Difficult Problems: You enjoy tackling challenges that don’t have obvious answers. You are curious, analytical and willing to experiment, learn and adapt.

Have Strong Analytical Skills: You can interpret data, identify patterns and think critically about business problems.

Are Passionate About Data and Technology: You’re excited by the opportunity to work with modern data platforms, cloud technologies and AI tools.

Enjoy Learning New Technologies:

Whether you’ve used them through university projects, personal projects, hackathons or internships, we’d love to hear about your experience with technologies such as:

• Python
• SQL
• Snowflake
• dbt
• Streamlit
• Machine learning frameworks
• Generative AI tools
• Copilot and AI agents
• Data visualisation platforms

Have an Interest in Fraud, Risk or Cybercrime:

You are interested in understanding how fraud evolves and how analytics, data science and AI can be used to combat increasingly sophisticated threats.

Are Collaborative Team Players:

You enjoy working with others, sharing ideas and contributing to a supportive and inclusive team environment.

Essential Requirements

• Studying a STEM‑related degree, including Data Science, Computer Science, Mathematics, Statistics, Physics, Engineering, or another quantitative discipline.
• Returning to university following the placement year.
• Right to work in the UK for the duration of the placement.

What Will Make You Stand Out:

• Personal data science or AI projects
• Open‑source contributions
• Hackathons or competitions
• Analytics portfolios or GitHub projects
• Experience building applications or dashboards
• Experience using Snowflake, dbt, Streamlit, machine learning or AI tools
• Curious exploration of fraud, cybersecurity or risk‑related topics

Show us something you’ve built, analysed, automated or improved.

We value practical evidence of learning and problem solving as much as academic achievement.

Learning & Development:

You’ll receive structured support throughout the placement, including:

• An immersive onboarding programme
• Technical training in data science, fraud analytics and consulting
• Mentorship from experienced professionals
• Exposure to customer‑facing projects
• Opportunities to collaborate with data scientists, analytics engineers and consultants
• Access to modern AI and analytics technologies used across the business

Our goal is to help you develop both technically and professionally, preparing you for a future career in data science, analytics, fraud prevention or consulting.

Life at LexisNexis Risk Solutions:

Our culture combines innovation, collaboration and continuous learning. We encourage people to challenge ideas, explore new technologies and take ownership of meaningful work.

You’ll join a community of graduates, placement students and experienced professionals who are passionate about solving complex problems and delivering real value to customers.

Diversity & Inclusion:

We value different perspectives and are committed to providing an inclusive recruitment experience. We welcome applications from candidates with diverse backgrounds and experiences.

If you require additional support at any stage of the application process, please let us know what adjustments would help.

Benefits

• Hybrid working
• 25 days annual leave, plus the option to buy more
• Graduate and placement networking events
• Volunteer and community engagement opportunities
• Wellbeing and mental health support

We’re looking for people who are excited by challenging problems, motivated to learn and eager to build the next generation of analytics and fraud prevention solutions.

We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.