Key Responsibilities

• Conduct in-depth analysis on large datasets to extract meaningful insights and identify trends related to risk and fraud.
• Create and enhance data models that help write and evaluate rules for detecting and mitigating risk.
• Partner with product and operational teams to understand key risk indicators and behaviors.
• Apply advanced statistical techniques such as Propensity Score Matching, Causal Impact analysis, and regression models to inform decisions and model complex scenarios.
• Use data visualization tools (e.g., Looker, Spark) to present findings effectively and communicate insights to stakeholders.
• Translate hypotheses into tools and processes that improve our detection capabilities.
• Iterate on models and analyses to refine detection strategies and improve accuracy and efficiency.

Required Qualifications

• Minimum of 9 years of experience as a Data Scientist or Data Analyst with a proven track record of extracting actionable insights from large datasets.
• At least 2 years of experience working in a risk and fraud environment, with a focus on detection and mitigation strategies.
• Mastery of SQL for data manipulation and querying.
• Proficiency in programming languages such as Python or R for data analysis and modeling.
• Experience with data visualization software such as Looker, Spark, or similar tools.
• Expert knowledge of sophisticated analysis techniques, including but not limited to Propensity Score Matching, Causal Impact analysis, and regression modeling.
• Strong analytical thinking with the ability to approach complex problems and develop creative solutions.

Preferred Skills

• Experience with machine learning frameworks and algorithms.
• Familiarity with data engineering concepts and tools.
• Knowledge of data compliance and privacy standards.

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