Junior Fraud Data Scientist

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.

• 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.