• Partner with senior scientists to forecast cash flows and other drivers of business health using statistical and ML modeling. • Design and build models that make our capital products better for customers and deliver positive return assets. Sharper pricing, lower losses, growth and offers that fit how SMBs actually run. • Proactively mine our datasets for insight, and prototype new models that move the needle for customers and the business. • Own model outcomes end-to-end. Not just deployment bespoke to the platforms Pipe serves (e.g., UberEats and Housecall Pro), but ongoing performance, residual analysis, drift detection, and the judgment calls about when a model needs to be retrained, replaced, or retired. You'll work closely with product, risk peers and engineering to make this real in production.

• Independently developing and owning statistical models that forecast cash flows and other drivers of business and customer health. • Designing and building models that optimize our product offerings to better serve our customers' needs. • Proactively exploring datasets to surface insights, and prototyping new approaches that create measurable value for customers. • Taking full outcome ownership of deployed models, including performance monitoring, residual analysis, and iteration, in close collaboration with engineering. • Mentoring junior team members, raising the technical bar through code and design review, and contributing to our data science best practices and long-term tooling roadmap. • Partnering with engineering and data engineering to productionize models, shape data infrastructure requirements, and improve our MLOps foundations.

• Define and own the KPIs that measure CX health, downstream impact, product experience, and user sentiment. • Analyze user behavior and marketplace dynamics to identify opportunities and inform CX product and agent team priorities. • Measure the tradeoffs between user experience and business value. • Translate complex data into actionable recommendations for product and leadership teams. • Partner with product managers and engineers to design, implement, and evaluate A/B tests and feature rollouts. • Develop frameworks for causal inference, impact measurement, and long-term ecosystem health. • Build scalable methodologies to understand feature performance and guide iteration. • Use our modern data stack to build dashboards, data pipelines, and self-serve tools that empower teams across Whatnot. • Partner with engineers and operations leadership to improve data accessibility, ensure data quality, and support instrumentation for new product features. • Advocate for data-driven decision-making and foster a culture of measurement across the CX organization. • Communicate insights clearly to both technical and non-technical audiences, influencing roadmaps and strategic decisions. • Serve as a thought partner to CX leads, shaping how we build, launch, and iterate on experiences across the platform.

• Design and implement scalable and reliable approaches to support or automate decision making throughout the business • Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear • Acquire data by building the necessary SQL / ETL queries and import processes through various company specific interfaces for accessing S3, RedShift, and Spark storage systems • Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies • Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks • Validate models against alternative approaches, expected and observed outcomes, and other business defined key performance indicators • Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production • Implement and deploy state of the art machine learning algorithms under Gen AI, build prototypes, troubleshoot customer issues, and explore new solutions • Interact closely with customers and with the academic community to drive innovation and deliver tailored data science solutions