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DocuSign's Product Data Science team is seeking a Senior Data Scientist to drive data-driven product development and decision-making across the Intelligent Agreement Management platform. In this individual contributor role, you'll partner with Product, Engineering, and UX teams to uncover insights, identify product improvement opportunities, and develop data science models that enhance customer experience and adoption.
Key responsibilities include collaborating with cross-functional teams to research and build data analyses that identify opportunities for product improvements and new features; serving as subject matter expert for product data strategy in assigned areas; working closely with product managers, researchers, engineers, and leadership to prioritize insights and data product opportunities; specifying and implementing product telemetry and defining KPIs with goals; owning regular reporting to track product usage and success metrics.
You'll partner with the Global Data Analytics team to automate data pipelines to Snowflake, design and build automated dashboards connecting internal and external data sources, and enable business users to self-serve on data needs. You'll design, administer, and analyze A/B tests and multivariate tests, develop actionable analytical insights for senior management, and evangelize models, frameworks, and insights across the organization.
Required qualifications: BA/BS in a quantitative field (Statistics, Math, CS, Economics, Finance) or equivalent; 8+ years in business/product analytics within SaaS/Cloud; proficiency with SQL for data analysis and validation; experience with data visualization tools (Tableau, Power BI, or similar); strong statistics and analytical techniques; proven ability to solve business problems with data; excellent communication skills for non-technical stakeholders; experience implementing product telemetry with engineering teams; familiarity with agile/scrum development.
Preferred: strong communication and leadership skills with cross-functional collaboration ability; experience with A/B testing, cohort analysis, and user segmentation; machine learning model design and refinement; Python or R proficiency; data engineering process familiarity; self-starter mentality in fast-paced environments.
