Data Product Manager - Payment Optimization

Company Overview Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM).

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.

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 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