• Lead the design, development, and deployment of ML models that predict and influence customer behavior — including churn, retention, activation, lifetime value, and lifecycle transitions. • Build behavioral segmentation frameworks, uplift models, and propensity scoring used across product, lifecycle marketing, and account strategy. • Drive insights for core product areas (audience targeting, measurement, bidding, onboarding, campaign performance) using a mix of causal inference, predictive modeling, and experimentation. • Own end-to-end analytics for key initiatives: partner with Product to define success metrics, guide instrumentation, design experiments, and quantify impact. • Design, build and deploy rigorous and statistically sound models and systems that help customers understand causal effects of their marketing strategies. • Influence product roadmaps using quantified opportunity sizing, behavioral funnel analysis, and deep dives into customer usage patterns. • Collaborate with Data Engineering and ML Platform teams to build scalable pipelines, datasets, and model operationalization workflows. • Act as a technical mentor for the broader analytics and data science team; elevate scientific rigor, model quality, and best practices.

• 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.