Senior Data Scientist, Current

• 5+ years of experience in data science with a strong emphasis on machine learning modeling
• Proficiency in Python for data analysis and ML, with experience using libraries such as Scikit-Learn, XGBoost, TensorFlow, or PyTorch
• Expertise in SQL and working with large, complex structured and semi-structured datasets
• Strong understanding of machine learning techniques, including logistic regression, gradient boosting, decision trees, and clustering methods
• Experience in feature engineering, model selection, and performance optimization
• Experienced in designing, executing, analyzing and reading out experiments
• Strong communication skills and ability to present findings to technical and non-technical stakeholders
• Master’s or higher degree in Data Science, Computer Science, Statistics, or a related field is preferred

Desirable

• As a Senior Data Scientist on the Banking team, you will focus on machine learning modeling, leveraging data to build predictive models that enhance our credit and liquidity products, improve customer experience, and grow revenue
• You will work on solving complex problems with a hands-on approach, developing and deploying machine learning models, and contributing to data-driven decision-making
• Your day-to-day will include analyzing, preparing and structuring data, building and optimizing predictive models, and collaborating with cross-functional teams to integrate machine learning solutions into banking products and processes
• Design, develop, and deploy predictive classification and regression models, anomaly detection models and algorithms for credit risk assessment, building underwriting policies and other use cases
• Conduct extensive EDA, feature engineering and data preprocessing to ensure high-quality input for ML models
• Collaborate with product, engineering, and business teams to integrate predictive models into workflows
• Evaluate and optimize model performance using statistical and ML techniques
• Conduct exploratory data analysis to identify patterns and insights that inform model development
• Design and execute A/B tests to measure and validate model impact
• Implement model governance techniques and build model monitoring dashboards
• Develop and implement model monitoring dashboards and model governance techniques
• Work with analytics and data engineering teams to ensure efficient data pipelines and model deployment
• Analyze diverse datasets to extract meaningful insights and patterns, identifying actionable opportunities for optimization and innovation