• Work remotely between the hours of 9 – 5 PST • Collaborate with internal stakeholders to understand their goals and objectives, enabling data-driven decision-making across the organization. • Utilize programming skills to explore and analyze data. • Identify patterns, trends, nuances, and potential data quality issues within complex datasets. • Apply supervised and unsupervised machine learning techniques to extract insights from data. • Communicate analysis findings and conclusions effectively to both technical and non-technical stakeholders. • Integrate datasets from diverse business sources into a unified format for analysis. • Develop custom queries to address ad-hoc and ongoing customer analytical requests.

• Work cross-functionally with Product and subject matter experts to conceptualize, prototype, and build data solutions • Connect disparate datasets (e.g. claims, contract rates, demographics data) to empower internal and external stakeholders • Build and maintain data engineering systems that support AI use cases, including scalable ingestion pipelines, feature generation, and downstream products • Contribute to building, maintaining, and testing data pipelines • Draft internal and external technical documentation • Seek and prioritize technical and product feedback from internal customers • Iterate quickly with an eye towards value

• Update & Maintain: Oversee the timely updates of the firm's CMS with completed tasks related to claims, medical records, police reports, and other firm-specific requirements. • Consistency is Key: Ensure that the CMS accurately reflects Finch's application, maintaining consistency and accuracy across platforms. • Real-Time Monitoring: Manage task updates in real-time to guarantee all information is current and precise. • Collaborative Problem-Solving: Work closely with team members to identify and resolve discrepancies in case management updates. • Record Keeping: Maintain detailed records of all updates and changes made to the CMS. • Adapt & Innovate: Embrace changes in processes and technology to enhance efficiency and accuracy.

• Build advanced recommendation models, such as Next Best Offer systems, leveraging cutting-edge techniques. • Create and own the end-to-end lifecycle of machine learning models, from data preprocessing and feature engineering to production deployment. • Design and deploy scalable machine learning architectures that integrate seamlessly into customer-facing applications. • Continuously evaluate and improve model accuracy, scalability, and computational efficiency. • Stay up-to-date with the latest advancements in machine learning to ensure best-in-class solutions are applied to business problems. • Report to the Director of Growth Data Science. • Work with the Growth Data Science team to drive measurable impact on user engagement and business outcomes. • Collaborate with cross-functional partners in Product, Engineering, Analytics, and Marketing to deliver data-driven solutions. • Identify new opportunities to uncover actionable insights from large-scale datasets. • Grow the effectiveness of the PLG funnel through optimized model performance and personalization.