• Conduct predictive analytics and forecasting • Work with NLP and deep learning • Build low latency systems for real-time processing • Collaborate with teams in various projects

• Join the Data & Analytics team within the Growth organization • Own complex forecasting challenges that directly impact our supply chain, marketing spend, and business growth • Build advanced demand forecasting models to solve operational challenges • Create marketing spend optimization algorithms to improve CAC targeting • Design experimentation frameworks for incrementality testing across marketing channels • Develop marketing attribution models handling complex customer journeys and multi-touch attribution

• Lead e2e analytics delivery: intake and clarification of requests, analytic design, development, validation, interpretation, and final delivery. • Translate stakeholder questions into analytic specifications (cohorts, definitions, assumptions, time windows, and sensitivity checks). • Build and review production-grade analytics using SQL and Python in a modern cloud analytics environment; apply distributed processing patterns (Spark/PySpark) when appropriate. • Establish and enforce analytic quality standards including reproducibility, documentation, peer review, testing, and reconciliation against benchmarks or historical trends. • Partner with research/domain leaders to ensure analytic outputs are interpreted correctly and include appropriate caveats and limitations. • Advise clients on the meaning of their data and recommend actions they should take. • Optimize analytic throughput by creating reusable code patterns, standard templates, and repeatable workflows. • Mentor and coach analysts and engineers on technical execution, analytic methods, and clear communication of results. • Develop dashboards, reports, and KPIs to support executive decision-making. • Collaborate with engineering leadership on performance, scalability, job orchestration patterns, data pipelines and data governance aligned to security and compliance needs. • Support secure handling of sensitive data by following access controls, least-privilege practices, and project-specific data use requirements.

• Develop predictive and prescriptive models to support pricing, risk, customer, and operational decision-making across the pet insurance portfolio • Apply advanced statistical and machine learning techniques to a variety of business problems, including customer behavior, retention, and marketing performance • Mentor other data scientists and analytics talent as needed, and develop data science rigor and expertise across the organization • Partner with actuarial, product, and analytics teams to set data science goals and objectives and translate business questions into analytical solutions • Collaborate with engineering and analytics teams to deploy models into production and ensure scalable, maintainable solutions • Communicate insights and recommendations to technical and non-technical audiences, leveraging learnings from insurance and other consumer-facing industries