Full-Time Data Scientist

• End-to-End Model Development: Own the entire lifecycle of fraud detection models, from data exploration and feature engineering to model training, validation, deployment, and monitoring.
• Advanced Analysis: Conduct deep-dive investigations into emerging fraud patterns and user behavior, using clustering, outlier detection, and other unsupervised techniques to uncover hidden risks.
• Experimentation: Design and execute A/B tests to measure the impact of new models, rules, and strategies on both fraud detection rates and user experience.
• Stakeholder Collaboration: Partner closely with Product, Engineering, and Operations teams to translate business needs into data science solutions and communicate complex results to non-technical audiences.
• Productionalize Models: Deploy, monitor, and maintain machine learning models in a cloud environment, ensuring high availability and performance.
• Reporting & Visualization: Build and maintain dashboards using tools like Tableau or Looker to track key performance indicators (KPIs) like fraud loss rates, false positive rates, and model performance

• Experience: 3-5 years of experience in a hands-on data science role, building and deploying machine learning models.
• Python: Expert-level Python for data analysis and modeling (pandas, scikit-learn, etc.).
• SQL: Advanced SQL skills for complex data extraction and manipulation.
• Machine Learning Modeling: Deep experience with tree-based ML models (XGBoost, CatBoost, LightGBM) and statistical models (Logistic Regression, Lasso/Ridge).
• Sampling Techniques: Strong understanding of sampling techniques for handling highly imbalanced datasets.
• Unsupervised Learning: Practical experience with clustering and outlier detection techniques (e.g., K-Means, K Nearest Neighbors, Isolation Forest).
• Model Lifecycle & Cloud: Proven experience with the full modeling lifecycle, including model deployment, monitoring, and maintenance on a cloud platform like GCP, AWS, or Azure.
• Communication: Excellent stakeholder management and communication skills, with a demonstrated ability to explain complex technical concepts to diverse audiences.
• Analytical Rigor: A solid foundation in statistics and experience designing and analyzing A/B tests.
• Communication: Advanced English level

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