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). What you'll do The GDA (Global Data Analytics) Data Scientist is a highly motivated self-starter who is responsible for designing, building, and promoting models and algorithms that power the next generation of machine learning and data science products for the various organizations at Docusign. You will be solving difficult and non-routine problems by applying analytical methods in novel ways; this includes processing, analyzing and interpreting large and sophisticated data sets, with an emphasis on actionable results. You will also need to collaborate closely with Sales GTM (Go To Market), Customer Success, Engineering and other teams to implement model-based solutions, measure the effectiveness of data products and drive growth and customer success. This role will influence and shape the design, architecture, and roadmap for predictive and prescriptive data products for the GTM teams. This position is an individual contributor role reporting to the Senior Manager, Data Science GDA. Responsibility Collaborate with a cross-functional agile team spanning data science, data engineering, product management, and business experts to build new product features that advance our mission to understand our platform and help us sustainably grow as a business Lead Data Science projects end-to-end, ensuring cross team collaboration and partnership with business Contribute to designing, building, evaluating, shipping, and refining our data products by hands-on ML development Build product recommendation systems that support the Docusign Agreement Cloud Manage data ingestion from multiple infrastructures Coordinate effective, quantitative strategies directly derived from communication with stakeholders Drive optimization, testing, and tooling to improve quality Design experiments that evaluate the effectiveness of data products Mentor junior members of the team on mathematical modeling and ML best practices Develop data preparation processes to consolidate heterogeneous datasets and work around data quality issues Communicate and present strategic insights to non-technical audiences Work within the Machine Learning platform team to deploy models to production using existing and emerging machine learning methods and technologies Work with stakeholders to translate product requirements into robust, customer-agnostic machine learning success metrics 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 Bachelor or Master’s degree in Physics, Mathematics, Statistics, Computer Science or related field 5+ years hands on experience in building data science applications and machine learning pipelines Experience with Python both for research and software development purposes Experience across the GTM domain as a Data Scientist Preferred Knowledge of common machine learning and statistics frameworks and concepts Experience with large data sets, distributed computing and cloud computing platforms Proficiency with relational databases (e.g., SQL) Ability to break down technical concepts into simple terms to present to diverse, technical, and non-technical audiences Experience in training and deploying machine learning models in production environments Knowledge of Apache Airflow, Spark, Snowflake Experience working with technologies like AWS, Git and Terraform MLOps experience Effective written and verbal communication skills Track record of driving successful data science initiatives while balancing the diagnosis and fixing of urgent problems with a thoughtful approach Experience creatively working with challenging data and systems Ability to deliver in a complex and fast-moving organization at a global scale Deeply analytical with a keen understanding of business processes and programs and the ability to translate data and insights into operational re