Risk Data Scientist, Robinhood

• Problem solving skills and a can-do attitude to dive deep into data to solve business problems
• Strong knowledge of quantitative finance models, including but not limited to asset volatility models, factor models, options pricing models
• Solid understanding of unsupervised learning, statistical analysis and machine learning algorithms for imbalanced datasets
• Strong data wrangling skills to produce analytics insights by exploring large scale data sets
• Excellent programming skills, including familiarity with Python (numpy, scipy, pandas)
• Experience communicating data driven insights
• Knowledge of model risk governance and experience collaborating with model validation teams to ensure compliance with regulatory requirements
• Experience with Tableau, Looker, and/or Mode
• Graduate degree in a quantitative field such as mathematics, economics, statistics, engineering or natural sciences (or equivalent research experience)
• 2-4+ years professional experience as a Data Scientist or machine learning engineer

Desirable

• Utilize advanced statistical and machine learning techniques, including gradient boosted machines, to analyze large datasets and identify patterns and trends related to credit risk in the brokerage lending space
• Conduct in-depth data analysis to evaluate credit and market risk factors, including credit history, financial statements, collateral valuation, market risks associated with financial assets and derivatives
• Build machine learning modeling pipelines to support data science in production, including validation & testing for quality control / quality assurance
• Monitor and evaluate the performance of risk models, making necessary adjustments and improvements as needed to enhance the accuracy and effectiveness of risk assessments
• Collaborate with cross-functional teams, including analysts, engineers, and product managers, to understand business requirements and develop effective risk mitigation strategies for brokerage lending activities
• History of efficiently deploying models into production quickly and without defect
• Leverage knowledge of regulatory requirements and compliance related to credit and market risk management, lending practices, options trading, financial derivatives, model risk governance, and model validation
• Ensure compliance with model risk governance policies and procedures, including model validation and ongoing model performance monitoring. Collaborate with model validation teams
• Communicate complex findings and recommendations to stakeholders, including senior management, in a clear and concise manner, enabling informed decision-making. Produce well-written documentation, including model white papers and analysis reports, to support decision-making