Back Growth Data Science AI Engineer

• Design, build, and maintain scalable data pipelines that ingest and process product telemetry and behavioral signals to support growth experiments and reporting

• Develop and maintain automated dashboards in Tableau to provide the growth squad with real-time visibility into experiment performance, funnel health, and AI model accuracy

• Utilize agentic AI tools like Claude Code to rapidly prototype features, refactor data processing scripts, and automate manual engineering workflows

• Implement and manage the technical frameworks required for A/B testing and causal inference, ensuring growth experiments are architected for accuracy and scale

• Partner with Engineering to ensure growth data is properly instrumented and flows correctly between business systems of record and Docusign’s product platforms

• Apply advanced techniques (predictive modeling, segmentation, LTV analysis) to understand customer behavior and integrate these insights into automated growth loops

Job Designation

Hybrid:Employee divides their time between in-office and remote work. Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation)

Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job. Preferred job designations are not guaranteed when changing positions within Docusign. Docusign reserves the right to change a position's job designation depending on business needs and as permitted by local law.

What you bring

Basic

• 5+ years of experience in Data Engineering, Data Science, or a related technical role, ideally within a SaaS growth environment

• Strong command of Python and SQL, with experience building production-grade data pipelines (e.g., using dbt, Airflow, or similar orchestration tools) and creating impactful visualizations in Tableau

• Experience using AI-assisted coding tools (like Claude Code or GitHub Copilot) to improve development velocity and code quality

• Deep understanding of data warehousing concepts, telemetry instrumentation, and API integrations

• Practical knowledge of experimentation frameworks, including A/B testing and statistical significance