Lead Data Scientist – Banking

Lead Data Scientist – Banking

Requirements

• 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.

Responsibilities

• 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.

Preferred Qualifications

• Experience using the Scala programming language for developing scalable and efficient machine learning solutions.
• 2+ years of experience building and deploying models using Vertex AI.
• Experience in B2C Fintech or Financial Services domain(s).