# LexisNexis — ML Industrial placement · Jul 2025 — Aug 2026 Role: Data Scientist · Industrial placement Stack: Python, SQL, Snowflake, Feature engineering, Representation learning During my industrial placement at LexisNexis Risk Solutions, I completed two fraud-model optimisations and analysed large-scale financial data using Python, SQL and Snowflake\. Alongside this applied work, I researched self-supervised fraud representations and gained practical experience in GPU compute and batch training\. ## Context Fraud modelling involves understanding complex financial behaviour, developing useful features and evaluating changes carefully\. ## My contribution Completed two fraud-model optimisations and analysed large-scale financial data\. Researched self-supervised representations alongside practical modelling work\. ## Approach - Used Python, SQL and Snowflake to analyse large-scale financial data\. - Worked across feature engineering, model tuning and evaluation for fraud-model optimisation\. - Investigated graph embeddings, contrastive learning and non-Euclidean geometry as approaches to self-supervised fraud representations\. - Gained practical experience in GPU compute and batch training\. ## Highlights - Completed two fraud-model optimisations, working across feature engineering, tuning and evaluation\. - Analysed large-scale financial data using Python, SQL and Snowflake\. - Researched graph embeddings, contrastive learning and non-Euclidean geometry for self-supervised fraud representations\. - Gained practical experience in GPU compute and batch training\. ## Evidence - Two fraud-model optimisations completed during the industrial placement, July 2025 to August 2026\. - Research into self-supervised fraud representations accompanied applied data-science work\. ## Limitations - Employer-confidential work: no transaction data, source code or internal evaluation results are published\. - Research directions are not claims of deployed models or measured performance uplift\. - Descriptions are owner-supplied; no independently verified results are claimed\. Source: Owner-supplied professional and project descriptions; earlier non-conflicting portfolio material retained\. Not independent verification\.