we are looking for a 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.
This role is designed for an analytical professional with 1-2 years of experience who wants to build a deep expertise in adversarial data, learn from an experienced team, and develop their technical skills across modern cloud data platforms.
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.
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.
This role is designed for an analytical professional with 1-2 years of experience who wants to build a deep expertise in adversarial data, learn from an experienced team, and develop their technical skills across modern cloud data platforms.
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.
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.
This position is open to all candidates.
