Data Scientist

Responsibilities

• Partner with Credit Risk to build production machine learning models; your models will determine who we lend to and how we interact with existing customers
• Assess the quality and risk of various model methodologies, algorithms, outputs, and business processes
• Develop our understanding of new data sources and how they may improve our existing processes and credit decisions
• Design, develop, and deploy infrastructure for the training, testing, and serving of models at scale
• Develop benchmark and challenger models to effectively challenge critical modeling decisions
• Develop explainability and monitoring tools to enable the responsible use of statistical machine learning models and adhere to regulatory guidelines
• Communicate your insights and solutions at all levels of the organization through effective presentations and technical reports
• Leverage multiple tools such as R, Python, and SQL on the Databricks platform to develop cutting edge models
• Use latest statistical and coding techniques on structured and un-structured data
• Identify business challenges and opportunities, using modeling and analytics to deliver strategic or tactical recommendations
• Partner with leaders to develop an enterprise modeling long term road map across the enterprise
• Be focused on execution, and ensure that models are implemented successfully and timely in production
• As a data scientist leader, be a mentor, lead junior analysts, offer guidance and provide training opportunities

Qualifications

• M.S. in statistics, mathematics, computer science, or other analytical field with 2-4 years of industry experience in statistical modeling and analytics, preferably in the Finance industry, or Ph.D in field mentioned above with 1-3 years of relevant experience
• 2+ years in data science, data analytics, applied machine learning, or related experience in a business setting; ability to convert ideas into testable hypotheses and/or next steps
• Deep understanding and experience with data analysis, and statistical and machine learning models
• Knowledgeable in applied statistics (e.g., hypothesis testing, regression techniques, probability, structured and un-structured learning algorithms, time series analytics, forecasting)
• Hands on experience with Python, Spark SQL, XGBoost, and Databricks a plus
• Strong Programming skills in Python and/or R (Python preferred)
• Predictive modeling experience with a quantitative background is preferred
• Experience working with consumer or business lending data is preferred
• Excellent problem solver, disciplined attention to detail, great communicator
• Strong ability to work proactively and collaboratively in a cross-functional team to drive results