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# Junior Fraud Data Scientist

- **Company:** Checkout.com
- **Location:** Greater London, UK
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Airflow, Data Analysis, BigQuery, Data Infrastructure, Data Systems, Data Warehousing, Python (Programming Language), Machine Learning, NumPy, Pandas, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations, Databricks
- **Published:** August 30, 2026
- **Apply:** https://www.collegerecruiter.com/job/2815445771-junior-fraud-data-scientist

## About the Role

* Experience: 1--2 years of hands-on professional experience as a Data Scientist or Data Analyst in a data-intensive environment.
 * Data Science Tech Stack: Solid proficiency in Python (Pandas, NumPy, Scikit-Learn) and strong capability writing and optimizing SQL queries.
 * Modern Data Infrastructure: Exposure to or basic hands-on experience working within environments like Databricks and data warehouses like BigQuery.
 * Academic Background: Degree in a quantitative field (Computer Science, Statistics, Data Science, Industrial Engineering, or equivalent).
 * Business-Impact Focus: An understanding of how to look past raw model metrics (precision/recall) to appreciate the operational impact of data decisions.
 * Communication: Fluent English with the ability to communicate technical findings clearly to team members., * Prior exposure to or hands-on projects involving machine learning models in a live, real-time production environment.
 * Familiarity with MLOps or orchestration tools such as MLflow or Airflow.
 * Previous domain exposure in FinTech, e-commerce, payments, or trust & safety.

## Description

As a Junior Fraud Data Scientist, you will contribute to our ongoing efforts to protect our ecosystem from financial threats and abuse. Working under the guidance of senior team members in a data-rich environment, you will assist in identifying malicious behaviors, support detection coverage, and help maintain our automated mitigation strategies., * Exploratory Data Analysis: Support the team by mining behavioral and transactional datasets to help identify anomalies and emerging fraud patterns.

 * Model Support & Optimization: Assist in building, tuning, and validating machine learning models (e.g., XGBoost, LightGBM) under the supervision of senior data scientists.
 * Feature Generation: Extract, engineer, and prepare new data features from structured and unstructured sources to help improve model performance.
 * Dashboarding & Monitoring: Build and maintain internal dashboards and pipelines to track model health, data drift, and key fraud KPIs.
 * Cross-functional Collaboration: Work alongside Fraud Analytics and Product teams to help translate operational fraud insights into automated data solutions.


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