At a Glance
- Tasks: Help protect our ecosystem from financial threats by analysing data and identifying fraud patterns.
- Company: Join a leading FinTech company focused on innovation and safety.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborative environment with mentorship from experienced data scientists.
- Why this job: Make a real impact in the fight against fraud while developing your data science skills.
- Qualifications: 1-2 years of experience in data science or analysis, with strong Python and SQL skills.
The predicted salary is between 30000 - 40000 £ per year.
About the Role
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.
- What You Will Be Doing
- 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, Light GBM) 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.
Requirements
- 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, Num Py, 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 Big Query.
- 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.
- Bonus Points
- 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 Fin Tech, e‑commerce, payments, or trust & safety.
- #J-18808-Ljbffr
Junior Fraud Data Scientist employer: Checkout.com
Checkout.com is an exceptional employer that champions a flexible hybrid working model, allowing employees to balance their professional and personal lives effectively. With a strong emphasis on growth and collaboration, the company provides ample opportunities for career development while working alongside talented teams in the dynamic financial services sector in London. Joining Checkout.com means being part of a forward-thinking organisation that values compliance and innovation in payments and product regulation.
