Posted a month ago
Description
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, 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.
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, 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.
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 FinTech, e‑commerce, payments, or trust & safety.
