Overview

We are seeking an experienced Data Scientist to take ownership of customer risk-scoring models and exploratory risk analysis. In this role, you will analyze complex behavioral data, conduct deep-dive investigations, and construct statistical models to protect both the business and its users.
Client:
A prominent global online gambling and entertainment enterprise focused on delivering secure, compliant, and data-driven experiences for its international customer base.
Project Overview:
Focused on enhancing customer safety and regulatory compliance, this project involves building, evaluating, and refining predictive risk-scoring models specifically for Anti-Money Laundering (AML) and Safer Gambling (SG) risk mitigation.

Responsibilities:
  • Design, evaluate, and iteratively refine predictive models targeting AML and Safer Gambling risk indicators.
  • Conduct exploratory data analysis and targeted investigations to identify behavioral anomalies and emerging risk patterns.
  • Partner with compliance and operational risk teams to translate risk domain insights into practical model features.
  • Clearly communicate analytical methodologies, model evaluation results, and strategic insights to non-technical stakeholders.
Required Qualifications:
  • 5+ years of professional experience in a Data Scientist role.
  • Strong technical proficiency in Python and SQL for complex data extraction, feature engineering, and statistical modeling.
  • Practical expertise in building and evaluating standard statistical and machine learning models (e.g., XGBoost, Linear/Logistic Regression).
  • Strong analytical problem-solving skills with a background in model evaluation metrics and data-driven investigations.
  • Effective verbal and written communication skills with the ability to articulate complex analytical concepts clearly.
Nice To Have:
  • Prior experience in online gaming.
  • Domain familiarity with Anti-Money Laundering (AML) regulations or Responsible/Safer Gambling frameworks.
  • Basic conceptual understanding of model deployment pipelines (hands-on engineering deployment expertise is not required).
Note:

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