• Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV). • Advanced Experiment Design: Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers. • Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible. • Deep Dive Analysis: Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends, and root causes of changes in key metrics, translating these findings into actionable product and business insights. • Develop Measurement Frameworks: Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes, ensuring consistent and scalable measurement across the organization. • Scaling Data Science: Partner with Data Engineering to design and build scalable, self-serve experimentation tooling and reusable analytical assets and frameworks (e.g., causal machine learning models) that empower other analysts and data consumers. • Strategic Influence: Act as a strategic thinker by translating complex statistical findings into clear, compelling, and actionable business narratives for cross-functional partners and senior leadership (VP/C-suite), driving strategic decisions and investment priorities. • Mentorship and Training: Serve as a technical subject matter expert, training and mentoring junior and mid-level data scientists on best practices in statistical rigor, experimental design, and causal modeling.

Role Description The Data Analyst will play a key role in extracting, transforming, and analyzing large complex claims and financial data to support business operations, operational reporting, and strategic decision‑making. This position works in close partnership with business partners, IT teams, and stakeholders from across the organization to deliver reliable insights, maintain data quality, and enable efficient data-centric processes. This role blends strong analytical thinking with technical skills, ensuring data accuracy, performance, and usability across systems. The ideal candidate is detail‑oriented, collaborative, and proficient with modern data tools, SQL, and visualization platforms. Essential Functions - Validation, transformation and analysis of structured and unstructured data from multiple internal and external sources, ensuring the highest standards of data accuracy and completeness. - Perform data research, profiling, and cleansing to support reporting and operational needs. - Analyze complex datasets and synthesize insights into actionable recommendations for business partners. - Perform detailed reconciliation across multiple data sources to validate accuracy, investigate discrepancies, and identify root‑cause variances, ensuring data integrity and reliable financial reporting. - Build recurring and ad hoc reports to support performance tracking, compliance, and business initiatives. - Compare and contrast data sources, trends, and historical results over time. - Participate in resolution of data integrity gaps by working with the business owners and IT. - Work with business partners to gather and understand functional requirements. - Develop complex queries and report out results. - Assist with data ingestion and integration tasks. - Work closely with business partners, IT teams, and project management functions to understand requirements and deliver insights. - Participate in operational or strategic discussions where data insights drive decision‑making. - Conduct root‑cause analysis on data issues and collaborate with stakeholders towards long‑term fixes. - Collaborate with business partners and technical teams to understand complex workflows and data dependencies. - Provide subject‑matter expertise on data patterns, business logic, and system interoperability. - Effectively collaborate and communicate with internal and external audit teams, providing clear explanations of data, processes, and controls while ensuring requests are addressed promptly and professionally to support a smooth and transparent audit process. - Collaborate with multiple cross functional teams and work on solutions which have larger impact on RiverStone business. - Establish and maintain effective working relationships, both internally and externally. Qualifications - Strong technical competence in Excel and data analysis tools. - Proficiency with SQL for data extraction and manipulation and ability to understand ETL Flows. - Ability to work with disparate datasets and perform complex data matching, merging, and validation. - Strong communication skills—able to explain findings to both technical and non‑technical audiences. - Detail‑oriented, highly organized, and able to manage multiple priorities. - Proven ability to learn through various methods, including instructor-led, self-taught, online learning, conferences, and books. - Self-starter when required, and able to deal with vague and ambiguous requirements. - Capable of working individually or collaboratively as part of a team. - Customer service oriented with the capability to develop long-lasting relationships with internal and external business partners. - Strong understanding of financial terminology and core accounting principles to accurately interpret datasets, reports, and operational metrics. Requirements - Encourages both self and team members to continuously improve RiverStone’s data business processes and systems, proactively suggesting ideas and solutions. - Consistently provides exceptional customer service to both internal and external business partners. - Demonstrates commitment to RiverStone’s core values and commitments. - Prioritizes and organizes tasks in a self-directed manner. - Acquires and maintains comprehensive knowledge of the assigned department. - Perform additional duties as needed, including collaborating with colleagues from other departments. Experience - 5+ years of experience in data analysis, data engineering, or analytics‑driven roles. - Prior experience supporting both operational and analytical use cases. - Experience contributing to or leading complex data initiatives. - Knowledge of reinsurance and claims business domains is beneficial. - Experience with BI/reporting platforms (e.g., Power BI) is a plus. - Bachelors or Masters degree in computer science, data analytics, information systems, statistics, or related field.

• Build and own machine learning powered features that directly improve our customer app and marketplace experience. • Work on customer-facing problems such as search and ranking, recommendations, segmentation, lifecycle modeling including activation, churn, and reactivation, lead scoring, and personalization. • Operate end to end, from shaping ambiguous product ideas into clearly defined problems, through modeling and experimentation, to deploying and monitoring production systems. • Contribute to data pipelines, partnering closely with engineers on real-time systems, and ensuring models are robust, scalable, and measurable against business KPIs such as conversion, retention, and revenue. • Collaborate cross-functionally with Product, Engineering, Marketing, and Pricing, acting as both a technical expert and a structured problem-solver who can clearly communicate trade-offs and impact.

• Analyze conversion across Medicare beneficiary acquisition funnel • Investigate drop-off points and identify high-conversion sources • Partner with operations to analyze call-center metrics • Examine post-enrollment retention and identify churn drivers • Translate business goals into structured problems and test plans • Build margin and unit-economics models for various metrics • Design scalable experimentation frameworks