Senior Data Scientist; Operations

The Role

As the Senior Data Scientist, you will lead initiatives targeted to drive efficiency, scalability, and operational performance using AI/ML. In this role, you will source, manage, and analyze large volumes of structured and unstructured data, synthesize results, and communicate/collaborate on the results with key stakeholders to drive strategic decision making in the organization. This role will require heavy collaboration across other data science, engineering, and analytics teams while also acting as an individual contributor as Operations build out Data Science skills and abilities.

The ideal candidate will be deeply immersed in data science with hands‑on modeling experience, driven by a passion for uncovering insights that fuel Prosper's growth and customer strategies. This role will be heavily invested in financial operations, particularly collections but inclusive of loan verifications, customer servicing, sales strategy, complaints, and customer experience. You will serve as subject matter expert on AI/ML in Operations and eventually cultivate a team of Data Scientists to deepen our strength in this space.

• Responsible for the design and development of AI/ML solutions in Operations.
• Spearhead innovation and application development to automate/optimize current workflows and processes for efficiency enhancement.
• Ensure the end‑to‑end solution meets all technical and business requirements.
• Expertise with modeling techniques: NLU/NLP, GenAI, ANN, Decision Tree, and other statistical modeling techniques.
• Develop creative ways to find and use data that significantly impact our key business metrics.
• Independently create proposals and identify opportunities to drive efficiency in operational processes and AI/ML.
• Design and execute tests having clear success metrics, be able to provide synthesized analysis of the results and manage solutions into production.
• Work cross‑functionally to shepherd projects from ideation into production working across Legal, Risk, Ops Teams, Product, Engineering to ensure efficiency.
• Develop and maintain procedures for tracking data sources, data lineage, data quality, completeness, redundancy vs. replication, data dictionary, data models, metadata and master data.
• Build production models used to generate ‘next best action’ strategies across customer journeys in Collections and Sales.
• Own operations priorities and roadmap, acting as liaison across all products and functional areas to deliver AI solutions for the organization.
• AI solutions could include, but not limited to, analytical deployments/proofs of concept, engineering‑developed AI solutions, and external AI service providers.
• Continuously develop and demonstrate improved modeling methodologies; advise on best practices and methods for evaluating and measuring experiments.
• Assess the potential usefulness and validity of new statistical approaches and data sources.

• Bachelor’s or higher degree in a quantitative field such as Mathematics, Statistics, Computer Science, Engineering, Finance, Economics, Physical Sciences or related technical field – a postgraduate degree is a plus.
• 7+ years of Data Science experience with 5‑7 years of experience in fintech/finance applying statistical and machine learning predictive techniques and 3+ years of analytics management experience.
• Expert level proficiency and experience in Python and SQL.
• Familiarity with Google Cloud services stack including Big Query, BQML, DBT, Looker, etc. (preferred).
• Experience with Git Hub and Bitbucket or similar source control mechanisms/tools (preferred).
• Hands‑on experience leveraging large sets of structured and unstructured data to develop data‑driven tactical and strategic analytics and insights using GenAI, ML, NLP, ANN and other modeling techniques.
• Solid understanding and habitual use of coding best practices / model documentation requirements.
• Strong communication and collaboration skills to communicate technical subject matter to other team members and Senior Management team.
• Proven ability to supervise and direct multiple data science projects in parallel and make critical decisions independently.
• A passion for solving complex problems in operations and executing test plans in a dynamic environment.

Flexible time off, comprehensive health coverage, competitive salary, paid parental leave. Wellness benefits including access to mental health resources, virtual HIIT and yoga workouts. A bevy of other perks including Udemy access, childcare assistance, pet insurance discounts, legal assistance, and additional discounts.

Applicants have rights under Federal Employment Laws. Family & Medical Leave Act (FMLA). Equal Employment Opportunity (EEO). Employee Polygraph Protection Act (EPPA). California applicants: please  to view our California Consumer Privacy Act (“CCPA”) Notice for Applicants, which describes your rights under the CCPA: