Data Scientist (Credit Risk Decision Science)

Responsibilities

• Design, develop, and evaluate predictive models and algorithms, with a strong focus on credit scoring.
• Identify meaningful insights from large datasets and metadata sources.
• Test hypotheses, analyze results, and interpret model performance.
• Develop and code basic programs, algorithms, and automated analytics pipelines.
• Participate in presenting analytical findings, model results, and recommendations to senior management and internal stakeholders.
• Exercise sound judgment and discretion within established procedures and best practices.
• Increase awareness of regulatory requirements—including Compliance and Fair Lending—and participate with relevant teams to validate models prior to implementation.
• Create high‑quality written and verbal documentation, including model development, performance, and governance materials.
• Build strong working relationships with team members and subject matter experts.
• 3+ years of experience in exploratory data analysis, including building tree‑based models.
• Strong programming and analytical skills, including experience with SQL, R, Python, SAS, and AWS.
• Basic understanding of financial services or banking concepts.
• Strong technical writing abilities, especially for model documentation.
• Strong data storytelling and technical presentation skills.
• Strong research, analytical thinking, and problem‑solving abilities.
• Ability to critically evaluate your own work, and handle complex problems.
• Strong communication and interpersonal skills.
• High initiative, sound judgment, and attention to detail.
• Technical proficiency in big data analytics, coding, project management, and technical writing.
• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field
• 3+ years of experience in financial services, particularly in acquisition model development, credit policy, or risk management.
• Experience validating models and partnering with internal or external auditors.
• Master’s in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related field.