Docusign, Inc. is an American software company headquartered in San Francisco, California that provides products for organizations to manage electronic agreements with electronic signatures on different devices. As of 2024, Docusign has about 1.5 million clients in 180 countries. Signatures processed by Docusign are compliant with the US ESIGN Act and the European Union's eIDAS regulation, including EU Advanced and EU Qualified Signatures.
\r\n\r\nDocusign, Inc. is an American software company headquartered in San Francisco, California that provides products for organizations to manage electronic agreements with electronic signatures on different devices. As of 2024, Docusign has about 1.5 million clients in 180 countries. Signatures processed by Docusign are compliant with the US ESIGN Act and the European Union's eIDAS regulation, including EU Advanced and EU Qualified Signatures.
\r\n\r\nIn April 2018, Docusign filed for an initial public offering. At the time of the IPO, the largest shareholders were venture investment firms Sigma Partners, Ignition Partners, Frazier Technology Ventures, and former CEO Keith Krach was the largest individual shareholder. None of the original founders are major shareholders. The company went public on the Nasdaq on April 27, 2018.
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Docusign, Inc. is an American software company headquartered in San Francisco, California that provides products for organizations to manage electronic agreements with electronic signatures on different devices. As of 2024, Docusign has about 1.5 million clients in 180 countries. Signatures processed by Docusign are compliant with the US ESIGN Act and the European Union's eIDAS regulation, including EU Advanced and EU Qualified Signatures.
\r\n\r\nDocusign, Inc. is an American software company headquartered in San Francisco, California that provides products for organizations to manage electronic agreements with electronic signatures on different devices. As of 2024, Docusign has about 1.5 million clients in 180 countries. Signatures processed by Docusign are compliant with the US ESIGN Act and the European Union's eIDAS regulation, including EU Advanced and EU Qualified Signatures.
\r\n\r\nIn April 2018, Docusign filed for an initial public offering. At the time of the IPO, the largest shareholders were venture investment firms Sigma Partners, Ignition Partners, Frazier Technology Ventures, and former CEO Keith Krach was the largest individual shareholder. None of the original founders are major shareholders. The company went public on the Nasdaq on April 27, 2018.
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Docusign, Inc. is an American software company headquartered in San Francisco, California that provides products for organizations to manage electronic agreements with electronic signatures on different devices. As of 2024, Docusign has about 1.5 million clients in 180 countries. Signatures processed by Docusign are compliant with the US ESIGN Act and the European Union's eIDAS regulation, including EU Advanced and EU Qualified Signatures.
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As a Technical Product Manager with a strong data and machine learning background in the payments domain, you will drive the analytical and ML strategy for Docusign’s subscription payments. You will frame payment problems as measurable hypotheses, design and interpret experiments with Data Science and Engineering, and translate model outputs into decisioning logic and customer-facing capabilities that improve payment success and retention across multiple processors. You will own the data and ML roadmap for initial and recurring payments, interrogate the data to uncover optimization opportunities, and regularly communicate strategy, experiment results and model performance to senior leadership.
This position is an individual contributor role reporting to the Product Management Director.
Responsibility
Own the data and ML roadmap for initial and recurring payments, identifying where statistical modeling, machine learning and AI automation create measurable business impact, and prioritizing based on expected value, data readiness and technical feasibility
Translate ML model outputs into shipped product features and concrete decisioning logic, for example turning routing or retry model scores into specific rules that determine how each transaction is processed and validating that those rules perform in production
Perform exploratory data analysis by writing SQL, building cohort and funnel analyses, and segmenting payment behavior to surface optimization opportunities rather than waiting for analysis to be handed to you
Design and own the experimentation program: define hypotheses, choose metrics, determine sample sizes and statistical power, guard against pitfalls such as peeking, multiple comparisons and novelty effects, and interpret results rigorously
Partner with Data Science to scope, evaluate and ship models such as payment routing, retry optimization and churn prediction, and define evaluation criteria (for example precision and recall, calibration and business-metric lift) before they ship
Establish the data foundation by defining instrumentation, data quality standards, feature definitions and monitoring required to train, evaluate and detect drift in production models
Translate model behavior and statistical findings into clear requirements and use cases for Engineering, Data Science and UX, driving initiatives from concept to launch
Monitor and act on KPIs such as payment success and authorization rates, passive churn, conversion, model precision and recall, and reconciliation accuracy
Build dashboards and self-serve analytics that make payment performance legible to leadership and partner teams
Partner with Legal, Risk, Finance, Engineering and Compliance to ensure responsible, explainable and auditable use of AI in payment decisioning
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.
Basic
Bachelors degree in Engineering, Computer Science, Statistics, Applied Math or a similar quantitative discipline
5+ years overall professional experience including approximately 3 years in digital product management
Experience in data science, analytics or ML engineering in addition to product management experience
Hands-on data analysis experience including fluency in SQL and comfort using Python or R to independently run analyses
Experience translating ML model outputs into shipped product capabilities or decisioning and routing rules and validating their impact in production
Experience working directly with UX, Engineering and Data Science or Testing teams on payment-related or other data-intensive initiatives
Knowledge of domestic and international payment options including cards, direct debits, alternate payment methods and gateways in a recurring or subscription environment
Experience with payment offerings in a global context and associated optimizations that reduce friction, drive conversion and improve retention
Preferred
Advanced degree (MS or MBA) in Engineering, Computer Science, Statistics, Applied Math or a related quantitative field
Hands-on experience with experimentation platforms (for example A/B testing frameworks), analytics or BI tools and modern data stacks
Direct experience with ML applications in payments such as authorization-rate optimization, intelligent routing, churn prediction or anomaly detection in reconciliation
Familiarity with causal inference methods (for example diff-in-diff, instrumental variables, uplift modeling) for situations where clean A/B tests are not possible
Familiarity with regulations and compliance standards (for example GDPR and PSD2) and their impact on payments, subscriptions and automated decisioning
Experience driving complex projects across a large, cross-functional organization
Written and verbal communication skills including the ability to translate technical findings for executives and lead through influence across teams