Data Scientist, Credit Risk Analytics
P
Prosper MarketplaceFintech
Working from our San Francisco or Phoenix offices or joining us as a fully remote team memberFull-TimeMiddle
SalaryA competitive salary and a 401(k) with a 5% company match
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Job Details
- Experience
- 2-3+ years
- Required Skills
- PythonSQLData AnalysisMachine LearningMLOpsRisk Management
Requirements
- 2-3+ years of work experience in fintech, finance, or a high-impact field applying statistical and machine learning predictive techniques.
- Advanced degree (M.S./Ph.D.) in statistics, computer science, engineering, economics, or related technical field.
- Expert knowledge of statistical programming languages, specifically Python.
- Expert knowledge of database languages, specifically SQL.
- Solid understanding of coding best practices, model documentation, and ML ops principles.
- Strong communication skills for translating technical subject matter into actionable business strategies.
- Strong ability to collaborate across functions including engineering, product, and compliance.
- Ability to work unsupervised in a fast-paced environment and prioritize parallel projects.
- Ability to innovate within regulatory guidelines with a commitment to model governance.
- Consumer lending experience in unsecured personal loans or credit cards is a plus.
Responsibilities
- Build industry-leading machine learning models for managing credit and fraud risks.
- Collaborate closely with engineering to deploy models into a production environment.
- Leverage complex data sources at scale to develop credit and fraud strategies to improve performance.
- Propose and execute strategic solutions to complex business problems within company objectives.
- Analyze ad-hoc portfolio performance and conduct root-cause analysis to identify trends.
- Communicate findings and recommendations to Risk Management and the broader organization.
- Develop internal tools and workflow solutions to increase data science productivity.
- Monitor credit risk models and strategies in production to extract actionable insights.
- Assess the validity of new machine learning algorithms and features from alternative data providers.
- Conduct high-impact ad-hoc analyses supporting risk management, operations, and corporate development.
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