Lead Data Scientist, Current

• The ideal candidate will possess a strong background in statistical analysis, machine learning techniques, and programming, and preferably a solid understanding of the Credit Risk domain and VertexAI’s capabilities, along with experience in deploying and scaling models using Scala and Google Dataflow
• 7+ years of experience in data science or machine learning roles, with expertise in developing and deploying machine learning models, preferably with a background in consumer credit risk and the lending space
• 2+ years of experience leading and managing data scientists and/or data analysts
• Degree in Computer Science, Statistics, Mathematics, or a related field
• Proficiency in Python programming and machine learning libraries (e.g., TensorFlow, scikit-learn) for model development and deployment
• Proficiency in data preprocessing, feature engineering, and model selection techniques to optimize performance
• Familiarity with Google Cloud Platform services, particularly VertexAI and Dataflow, for scalable data processing and model training
• Proficiency in SQL and at least one programming language for developing scalable and efficient machine learning solutions
• Understanding of credit risk models and methodologies
• Strong leadership, communication, and collaboration skills
• Ability to work effectively in a fast-paced, cross-functional environment
• Strong understanding of statistical analysis, hypothesis testing, and experiment design
• Excellent problem-solving skills with the ability to translate business requirements into technical solutions

Desirable

• We are in search of an experienced Data Scientist to lead our ML model development for our lending and liquidity products
• In this role, you will leverage VertexAI to lead the design, development, deployment, and governance of machine learning models that power and optimize our risk underwriting to achieve business objectives
• Design, develop, and deploy models that are used for risk underwriting, risk management, limit setting, pricing, and customer acquisition and engagement optimization
• Analyze diverse datasets, including but not limited to credit bureau, customer behavior, and alternative data, to extract meaningful insights and patterns, identifying actionable opportunities for optimization and innovation
• Lead and mentor a team of data scientists and analysts, providing guidance and support
• Conduct exploratory data analysis, feature engineering, and model selection to optimize performance and enhance predictive accuracy
• Implement and maintain scalable machine learning pipelines and workflows on Google Cloud Platform, ensuring reliability, scalability, and efficiency
• Leverage Google Dataflow to process large-scale data and build scalable data processing pipelines
• Evaluate model performance and govern deployed models using appropriate metrics, techniques, and scalable automated methods
• Maintain model documentation and perform ongoing refits/retraining of existing models to ensure models are always at or near optimal performance and risks of degradation are avoided or managed
• Design experiments, measure, test, and conduct readouts to optimize and test the underwriting process, offers, and customer engagement
• Collaborate with engineering teams to integrate machine learning models into production systems, monitor performance, and troubleshoot issues
• Contribute to developing best practices, standards, and documentation for machine learning processes and methodologies
• Develop and implement data-driven strategies for credit risk management