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 security.
- Benefits: Competitive salary, flexible hours, remote work options, and growth opportunities.
- Other info: Collaborative team environment with opportunities to work on cutting-edge machine learning projects.
- 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, strong Python and SQL skills.
The predicted salary is between 63000 - 77000 £ 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 in London 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.
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We think this is how you could land Junior Fraud Data Scientist in London
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We think you need these skills to ace Junior Fraud Data Scientist in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Checkout.com, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Checkout.com. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Checkout.com
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
