Staff Data Scientist - Machine Learning

The Role

The Risk and Operations Data Science team is looking for a Data Scientist to develop advanced machine learning 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 support credit underwriting, enhance loan processing, prevent login fraud and account take over, and support loss mitigation, etc. These tasks involve developing complex business rules to researching and applying state of the art machine learning 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.

What You'll Do

• Develop, implement, and continuously improve machine learning models and strategies that support various credit and operational procedures including but not limited to underwriting, loan processing, loss mitigation, and fraud/account takeover, etc

• Proactively identify opportunities to apply advanced machine learning approaches (e.g., NLP, Image Recognition, Graph Mining, etc) to solve complex business problems

• Explore and leverage in-house, external, and other open-source machine learning software/algorithms

• Collaborate with Model Risk Management team to demonstrate models are developed with high level rigor that satisfy Model Risk Management and Governance requirements

• Work closely with the Product and Engineering teams for model deployment

• Perform ongoing monitoring of the models through the construction of dashboards and KPI tracking

• Present model performance and insights to Credit, Risk, and Business Unit leaders

  
  

What You'll Need

• Bachelor's degree in Computer Science, Statistics, Mathematics, Physics, Engineering, or quantitative field required. Master's or Ph.D. degree preferred.

• 5+ years of relevant work experience with building and implementing machine learning models

• Excellent knowledge of machine learning and statistical modeling methods for supervised and unsupervised learning. These methods include (but not limited to) regression, clustering, outlier detection, novelty detection, decision trees, nearest neighbors, support vector machines, ensemble methods and boosting, neural networks, deep learning and its various applications. Continuously follow the advancement of machine learning and artificial intelligence to update your knowledge and skills in order to solve business problems with the most efficient methodologies

• Strong programming skills in Python

• Strong knowledge of databases and related languages/tools such as SQL, NoSQL, Hive, etc.

• Effective communication skills and ability to explain complex models in simple terms

Nice To Have

• Experience in a financial organization

• Experience with model documentation and delivering effective verbal and written communication

• Experience in working closely with Product, Engineering, and Model Risk Management teams