• As a Staff Data Scientist at PandaDoc, you will serve as a senior analytical leader, embedding yourself deeply in our product and business data to uncover non-obvious insights and drive actionable recommendations. • A primary focus of this strategic role is to champion and drive the organizational shift toward a data-driven culture. • You will own the advancement of our experimentation capabilities, train other analysts and data scientists on causal methodologies, and leverage your expertise to provide leadership with a clear, reliable understanding of true impact and causality. • You will report to the Director of Product Data and act as a strategic thought partner to Product, Finance, Design, Engineering, Product Marketing, and executive leadership, ensuring alignment between data insights and critical business decisions. • 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). • Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods. • Apply advanced causal inference techniques to scenarios where randomized controlled trials (RCTs) are infeasible. • 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. • Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes.

• The Clinical Data Manager II plays an essential role in the efficient design of project databases, the integration of data from multiple sources, and the reporting and analysis of key study data metrics. • The incumbent will recommend and drive solutions for database design and data reporting. • This role is critical for meeting sponsor study data endpoints and requirements. • Design study CRFs and databases utilizing eCRF library. • Create study Data Management Plan and deployment roadmap. • Monitor study timelines and communicate risk. • Develop database edits, rules, and derivations. • Prepare tracking reports and metrics. • Facilitate study team involvement in database development. • Prepare data extracts and processing for sponsor deliverables. • Communicate with sponsors regarding study data processes, endpoints, and overall data management process. • Carryout any other reasonable duties as requested.

• Develop and maintain custom reports in Oracle HCM using SQL to support HR operations and analytics. • Write advanced SQL queries to extract and transform HR data from Oracle tables. • Analyze HR datasets for workforce insights (headcount, attrition, recruiting, talent & succession planning, engagement, team performance). • Ensure accuracy and consistency of HR data across systems. • Perform advanced analytics and modeling in Databricks using Python and SQL. • Leverage Databricks notebooks for data exploration, transformation, and machine learning. • Publish curated datasets from Databricks to BI tools (Power BI) for HR scorecards and executive reporting. • Apply Python and statistical techniques for predictive modeling (e.g., attrition forecasting, workforce planning). • Use Databricks ML and Delta Lake for scalable analytics. • Identify trends and deliver actionable insights to HR leadership. • Partner with HR leaders to translate business questions into data requirements. • Work closely with Data Engineering and Platform teams to ensure HR data is clean, standardized, and accessible. • Document processes and create user-friendly guides for HR stakeholders. • Understand and apply PII masking and compliance requirements for HR data. • Ensure adherence to privacy regulations (GDPR, CCPA) and internal security standards.

You’ll also help design and manage AI-powered agents that support analytical and forecasting workflows, with a strong emphasis on control, evaluation, and real operational value. This is a hands-on, senior role with clear ownership and accountability. Responsibilities - Build predictive, forecasting, and risk models to support policy decisions, agency strategy, and cash-flow planning. - Own the full production lifecycle of models, including deployment, monitoring, drift detection, and retraining. - Translate model outputs into decision-ready insights for policy, finance, and operations teams. - Partner closely with data engineering and product to ensure models are supported by scalable, reliable data pipelines. - Design and manage AI/LLM-based agents to automate or augment analytical workflows. - Define evaluation metrics, guardrails, and failure modes for agent-driven systems. - Improve model reliability, interpretability, and business impact as the platform scales.