Lead Analyst - LTV
November 21
Description
• Monitor the accuracy of ML model projections by building dashboards and new monitoring frameworks • Analyze projections to identify and diagnose discrepancies between forecasted and actual performance • Drive model improvements by conducting feature research and enhancing SQL-based forecasting methods • Provide weekly and monthly business updates to senior leadership on model performance • Contribute to stakeholder-facing dashboards and disseminate high-level findings • Minimize human-in-the-loop systems by automating repetitive tasks • Enhance anomaly detection systems to inform the team of model performance concerns and spur timely remediation • Work cross-functionally to answer ad hoc questions regarding model performance and anomalies • Work with data scientists and engineers to build and maintain rigorous model testing methodologies • Identify opportunities to introduce or improve analytical methodologies to drive value • Build strong relationships with cross-functional partners to maintain alignment and awareness across initiatives
Requirements
• 5+ years of work experience in analytics (prior insurance experience preferred) • Expertise in SQL and BI software (Tableau, Mode, Power BI, etc.) • Bachelor’s degree in Mathematics, Statistics, Computer Science, or another quantitative field • Superior problem-solving skills with the ability to think strategically and innovatively • Exceptional written and oral communication; able to effectively collaborate with and present materials to Director and VP levels, strong data visualization skills • Detail-oriented; able to multitask across multiple domains; consistently maintains rigorous documentation • Roll-up-the sleeves work ethic and “do-what-it takes” attitude to efficiently execute and drive results in a fast-paced work environment • Embody best practices through automation and by leaning into reproducible processes • Ability to set quarterly goals and independently assess strategic options within familiar domains • Experience assessing machine-learning model performance and conducting root cause analysis • Understanding of insurance pricing and unit economics (preferred but not required) • Familiarity with Python and version control software (e.g., Git) (preferred but not required)
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