Data Scientist, Credit Risk Analytics

Your role in our mission

Prosper is seeking a Data Scientist under the Credit Risk Analytics vertical. You will become a core contributor with machine learning expertise in the credit risk team, delivering results that directly impact business value. This is a unique, hybrid role where you will not only build and deploy industry-leading predictive models but also play a critical role in shaping our credit risk strategy and business decisions.

How you’ll make an impact

• 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 (e.g., credit bureau reports, customer-supplied information) at scale to develop credit and fraud strategies to improve the credit performance and optimize risk decisions.
• Propose and execute strategic solutions to complex business problems, operating effectively within constraints and aligning with broader company objectives.
• Analyze ad-hoc portfolio performance at a granular segment level on an ongoing basis. Identify trends and conduct root-cause analysis to isolate key performance drivers. Communicate findings and recommendations to the Risk Management and broader Prosper community.
• Help the team develop internal tools and workflow solutions to increase data science productivity and operational efficiency.
• Actively monitor credit risk models and strategies in production, extracting actionable insights to significantly impact key business metrics.
• Assess the potential usefulness and validity of new machine learning algorithms and features sourced from diverse, alternative data providers.
• Conduct high-impact, ad-hoc analyses supporting risk management, investor services, operations, and corporate development initiatives.

Skills that will help you thrive

• 2-3+ years of work experience in fintech, finance, or another high-impact field applying statistical and machine learning predictive techniques. Consumer lending experience in unsecured personal loans or credit cards is a strong plus.
• Advanced degree (M.S./Ph.D.) preferably in statistics, computer science, engineering, physical sciences, economics, or a related technical field.
• Expert knowledge of statistical programming languages (e.g., Python) and database languages (e.g., SQL).
• Solid understanding of coding best practices, model documentation, and ML ops principles.
• Strong communication skills with the ability to translate complex technical subject matter into clear, actionable business strategies for cross-functional partners and senior management.
• Strong ability to collaborate seamlessly with people across various functions (engineering, product, compliance) and build strong relationships.
• Ability to work unsupervised in a fast-paced environment, effectively prioritizing among parallel technical and strategic projects.
• Ability to innovate within regulatory guidelines with a strong commitment to reproducible research and model governance.
• Self-motivated, results-oriented, enthusiastic, and a creative thinker who bridges the gap between data science and business strategy.

Resources to help you prosper

• A connected experience: We prioritize high-touch collaboration and flexibility. Whether you are working from our San Francisco or Phoenix offices or joining us as a fully remote team member, we provide the digital-first tools and intentional culture to keep you synced and supported
• Invested in your future: A competitive salary and a 401(k) with a 5% company match to help you build long-term financial security
• Holistic well-being: We provide the resources you need to thrive, from flexible time off and paid parental leave to an annual wellness allowance and comprehensive health coverage
• Professional & personal growth: Take advantage of a suite of premium perks, including Udemy access, childcare assistance, pet insurance, and a bevy of additional savings through Beneplace

Interview Process

• Recruiter Call: A brief screening to discuss your experience and initial questions.
• Department Interview: Deeper dive into technical skills and project alignment with the Hiring Manager or team member.
• Team/Virtual Interview: Meet team members for collaborative discussions, problem-solving, or technical exercises.
• Final Round: Discussion with a department head/executive.