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Social Finance, LLC seeks Staff Data Scientist in New York, NY:
Job Duties: 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.
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. Telecommuting is an option. Some travel to Sofi
offices may be required.
Minimum Requirements: Master’s degree (or its foreign degree equivalent) in Computer
Science, Data Science, or a related quantitative discipline, and two (2) years of
experience in the job offered or in any occupation in related field.
Special Skill Requirements: (1) Machine Learning and statistical modeling for
supervised and unsupervised learning; (2) Python; (3) Databases and related
languages and tools including SQL, NoSQL, and Hive; (4) Model Implementation; (5)
Model Monitoring; (6) Statistics; (7) Data Analysis. Any suitable combination of
education, training and/or experience is acceptable. Telecommuting is an option.
Some travel to Sofi offices may be required.
Salary: $247,200.00 - $284,280.00 per annum and standard company benefits.
Submit resume with references to: Req.# 24-146863 at: ATTN: HR, [email protected]
or apply through this posting.
Application Deadline: 11/04/2025
#LI-DNI
SoFi New York, New York, USA Office
Our New York office, located in the meat-packing district, provides an open work environment and is a hub for our finance teams. Our large conference, flexible work spaces, huddle rooms, and shared community areas allows our teams to openly collaborate while looking at some spectacular views.
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