• Champion a “data as a product” mindset: Set the standard for robust documentation, testing, and data model design – making revenue data products easy to understand, trust, and extend. • Mentor and uplevel the team: Share best practices in analytics engineering, code quality, and documentation; coach others to adopt product-minded approaches and raise AE standards across the org. • Partner cross-functionally with Finance, Data Science, Data Engineering, and Revenue stakeholders to define the future vision for key revenue data products, surfacing opportunities for innovation and improved business impact. • Continuously improve business-critical analytics workflows for revenue that power Investor Relations reporting and all downstream revenue consumers, ensuring reliability, scalability, and transparency. • Drive root-cause analysis and ask “why” to deeply understand the drivers of revenue data issues, proactively identifying improvements to workflows, code, and models. • Implement and advocate for orchestration best practices to ensure the reliability and timeliness of revenue data pipelines. • Leverage AI tools (e.g., Claude Code, Cursor, MCP servers) to boost developer productivity and workflow efficiency, and share learnings with the team. • Contribute to the adoption of modern AE standards (e.g., WAP—Write Audit Publish) and help define best practices for documentation, testing, and code review.

• Conduct descriptive and exploratory analyses to support product, growth, and business decisions. • Build and maintain dashboards and reports to monitor KPIs, experiments, feature launches, and overall performance. • Partner with Product and Growth teams to analyze user behavior, funnels, and retention. • Design and maintain clean, well-documented data models in Snowflake and dbt. • Improve metric definitions and ensure consistency across teams and dashboards. • Contribute to the reliability and scalability of our analytics infrastructure. • Support experimentation efforts (A/B testing) by helping define metrics, analyze results, and interpret outcomes. • Help teams frame the right questions and turn data into clear recommendations. • Communicate insights clearly to non-technical stakeholders. • Contribute to a strong data-driven culture through documentation, presentations, and workshops. • Act as a thought partner for teams making strategic or operational decisions.

• Collect and understand business requirements and translate those requirements into an actionable data warehouse plan. • Knowledge of multi-dimensional and tabular design patterns and ability to identify solutions. • Work within the SDLC framework in multiple environments. • Define and implement best practices across database design and ETL. • Direct ETL development, demonstrating an understanding of key concepts of ETL/ELT.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description GAINSCO is seeking a Predictive Analytics Manager to lead the development of advanced models that inform pricing, underwriting, and strategic growth. This role blends technical expertise with business acumen, supporting cross-functional teams and regulatory compliance in a dynamic insurance environment. - Build predictive models for loss cost, retention, lifetime value, and more using internal and external data. - Apply advanced statistical and machine learning techniques (GLMs, GBMs, decision trees, clustering, random forests). - Present insights and recommendations to senior leadership and business stakeholders in a clear, actionable format. - Monitor model performance and implement iterative improvements. - Create documentation for regulatory filings and respond to Insurance Department inquiries. - Collaborate with Product, Actuarial, and Claims teams to align analytics with business goals. Qualifications - Bachelor’s degree required, preferably in Mathematics, Actuarial Science, Risk Management, Economics, Computer Science, Information Management, or Statistics. - 8 or more years of experience in insurance product development, actuarial, or product management within the personal auto insurance industry required. - 5 or more years’ progressive data science experience in designing, developing, evaluating, and deploying predictive modeling, machine learning and advanced analytics required. - Experience with structured and unstructured data analysis is required. - Proficiency with Python and SQL required. Requirements - Understanding of statistical and predictive modeling techniques such as GLMs, machine learning, decision trees, clustering, forests, and neural networks and their application to business decisions. - Proven skills as a business consultant who uses modeling skills to answer business questions and drive profitable growth. - Ability to convey complex topics and results to non-technical audiences. - Ability to adapt quickly to changing timelines. Benefits - Excellent benefits package including medical & dental, vision insurance, life insurance, short-term and long-term disability insurance. - Parental Leave Policy. - 401K + Company Match. - PTO Plan + Paid Company determined Holidays.