Staff Data Scientist, SoFi

• Bachelor’s degree in Computer Science, Statistics, Econometrics, Mathematics, Physics, Engineering, or quantitative field required. Master’s degree preferred
• 5-10 years of relevant work experience with building and implementing machine learning and statistical models
• Excellent logic reasoning and communication abilities when interpreting business requirements and translating them into effective data solutions
• Strong skills in writing efficient SQL queries and Python code to create complex attributes, especially with large datasets
• Strong sensitivity to details in data and proactively investigate them to uncover unknown patterns
• Strong knowledge of databases and related languages/tools such as SQL, NoSQL, Hive, etc
• Demonstrated sophisticated experience in building efficient and reliable pipelines that interact with large datasets stored in SageMaker and Snowflake, automating recurring processes such as data extraction and processing, feature selection, model training, model monitoring, and generating documentation templates to support reproducibility and cross-functional collaboration
• Experience in working closely with Product, Engineering, and Data Engineering teams

Desirable

• The Risk Data Science team is looking for a Senior Staff Data Scientist to develop advanced machine learning and statistical models, guide measurement, strategy, and data-driven decision making to support various credit risk and operational areas at SoFi
• The Data Scientist will work closely with Credit, Risk, Product, Engineering, and Operations teams to design solutions to enhance the portfolio management, loss mitigation, and loss forecasting, etc
• These tasks involve developing complex business rules to researching and applying state of the art modeling methodologies to solve complex business problems
• This role is very rewarding as your work will have a direct and immediate impact on the business’ profitability
• Develop, deploy, and continuously improve machine learning and statistical models and strategies that support various credit and operational procedures including but not limited to portfolio management, loss mitigation, loss forecasting, etc
• Present model performance and insights to Credit, Risk, and Business Unit leaders
• Proactively identify opportunities to apply advanced modeling approaches to solve complex business problems
• Collaborate with Model Risk Management team to demonstrate models are developed with high level rigor that satisfy Model Risk Management and Governance requirements
• Perform ongoing monitoring of the models through the construction of dashboards and KPI tracking
• Collaborate with Data and Engineering teams to improve the model development, deployment, monitoring, and model re-calibration/re-build process
• Explore and leverage in-house, external, and other open-source machine learning software/algorithms