Title: Senior Data Scientist - Growth Marketing Location: San Francisco, California; Seattle, Washington Job Description: 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 We're looking for a hands-on, execution-minded Lead Data Scientist to join Docusign's Marketing Measurement team. You will own the full range of data science and analytics work strategic analysis, experimentation, stakeholder insights while also building the modeled data layers, operational dashboards, analysis agents, and productized integrations that make measurement self-serve and scalable. In this high-impact position you will own the productization layer for marketing measurement, architecting data infrastructure that makes sophisticated methodology accessible at scale. You will independently drive complex cross-functional initiatives from definition through adoption. Over time you will extend these practices across the broader team, operating as an internal center of excellence for how we model, build, and ship data products. Our ideal candidate combines strong analytical foundations with a product mindset someone who thrives at the intersection of data modeling, data product design, and marketing measurement, with a bias for shipping working assets over polishing decks. This position is an individual contributor role reporting to the Director, Marketing Data Science. Responsibility - Serve as DRI for measurement product initiatives end-to-end scoping, building, shipping, driving adoption across Marketing and Sales - Architect and maintain the data modeling layer for marketing measurement clean, tested, documented models that the whole team builds on - Productize measurement outputs into dashboards, automated reports, and analysis agents that stakeholders use without analyst intervention - Push insights into end-user tools (CDPs, marketing platforms, CRM) so measurement drives action at the point of decision - Partner with the pod's technical lead on methodology; translate attribution and incrementality science into production-grade data assets - Define and enforce modeling standards (naming, testing, documentation) that scale across pods - Collaborate with Data Engineering on pipeline reliability, orchestration, and upstream data quality - Report regularly to executive stakeholders, surfacing measurement results with clear next steps 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 - 8+ years of experience in analytics, data science, or marketing measurement - Experience pulling insight from raw data and shipping durable models and products - Experience with systems thinking, including how channels interact, where data flows break, and which upstream changes cascade downstream - Experience writing sophisticated SQL, including well-architected models and clearly documented transformations - Experience building structured data layers in Snowflake and dbt (or equivalent), including semantic/metrics definitions that keep reporting consistent across tools - Experience with marketing measurement concepts (attribution, incrementality, media mix) - Experience with advanced SQL and Python (or R); production experience with dbt, Airflow, or similar - Experience with a BI platform (Tableau, Looker, Hex, or equivalent) Preferred - Experience in B2B SaaS - Ability to ship early and iterate working assets over polished decks - Ability to see fragmented data sources as an opportunity to build something coherent the business relies on - Ability to exercise sharp judgment about what to measure and when "good enough" is the right call - Multi-touch attribution, media mix modeling, or incrementality testing at scale - CDPs (ActionIQ, Segment) and marketing platforms (Marketo, Salesforce) - Causal inference methods geo experiments, synthetic control, difference-in-differences - MS or PhD in a quantitative discipline (Statistics, Economics, Computer Science) Wage Transparency Pay for this position is based on a number of factors including geographic location and may vary depending on job-related knowledge, skills, and experience. Based on applicable legislation, the below details pay ranges in the following locations: California: $146,400.00 - $235,375.00 base salary Washington, Maryland, New Jersey and New York (including NYC metro area): $140,100.00 - $206,775.00 base salary This role is also eligible for the following: - Bonus: Sales personnel are eligible for variable incentive pay dependent on their achievement of pre-established sales goals. Non-Sales roles are eligible for a company bonus plan, which is calculated as a percentage of eligible wages and dependent on company performance. - Stock: This role is eligible to receive Restricted Stock Units (RSUs). Global benefits provide options for the following: - Paid Time Off: earned time off, as well as paid company holidays based on region - Paid Parental Leave: take up to six months off with your child after birth, adoption or foster care placement - Full Health Benefits Plans: options for 100% employer paid and minimum employee contribution health plans from day one of employment - Retirement Plans: select retirement and pension programs with potential for employer contributions - Learning and Development: options for coaching, online courses and education reimbursements - Compassionate Care Leave: paid time off following the loss of a loved one and other life-changing events Life at Docusign Working here Docusign is committed to building trust and making the world more agreeable for our employees, customers and the communities in which we live and work. You can count on us to listen, be honest, and try our best to do what's right, every day. At Docusign, everything is equal. We each have a responsibility to ensure every team member has an equal opportunity to succeed, to be heard, to exchange ideas openly, to build lasting relationships, and to do the work of their life. Best of all, you will be able to feel deep pride in the work you do, because your contribution helps us make the world better than we found it. And for that, you'll be loved by us, our customers, and the world in which we live. Applicant and Candidate Privacy Notice States Not Eligible for Employment This position is not eligible for employment in the following states: Alaska, Hawaii, Maine, Mississippi, North Dakota, South Dakota, Vermont, West Virginia and Wyoming. Equal Opportunity Employer It's important to us that we build a talented team that is as diverse as our customers and where all employees feel a deep sense of belonging and thrive. We encourage great talent who bring a range of perspectives to apply for our open positions. Docusign is an Equal Opportunity Employer and makes hiring decisions based on experience, skill, aptitude and a can-do approach. We will not discriminate based on race, ethnicity, color, age, sex, religion, national origin, ancestry, pregnancy, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, registered domestic partner status, caregiver status, marital status, veteran or military status, or any other legally protected category. EEO Know Your Rights poster #LI-Hybrid

• Manufacturing 8–10 years in manufacturing (optional) with hands-on experience in shop floor operations, production planning, and systems including MES, SCADA, and ERP. • Proficient in industrial protocols (OPC-UA, MQTT, Modbus) with ability to bridge OT/IT systems for real-time data extraction. • Applied experience with OEE, Six Sigma, SPC, and lean methodologies to drive measurable gains in yield, uptime, and efficiency. • Skilled in building scalable cloud data pipelines for high-volume manufacturing and IoT data using Spark, Kafka, Airflow, and Delta Lake. • Proficient in scikit-learn, TensorFlow, or PyTorch with experience moving models from prototype to production in industrial environments. • Strong communicator — able to translate complex model outputs into clear, actionable recommendations for operations and executive stakeholders. • Solid grounding in statistical methods — time series, regression, clustering, and hypothesis testing applied to manufacturing quality problems. • Experience designing A/B experiments and simulations to validate process changes and quantify business impact before full deployment.

Role Description The Emerging and Strategic Solutions ART is looking for a Data Scientist specializing in Generative AI who will be at the forefront of transforming our business segment through the design, development, and deployment of production AI applications. You will architect and ship agentic AI systems that transform how our operational areas work, including applications that can reason over business context, take action through tools and integrations, and automate complex workflows end to end. This role calls for a hands-on builder with deep experience taking LLM applications from prototype to production, supported by strong full-stack engineering, MLOps, and cloud infrastructure skills. This position demands a proactive problem solver who thrives in ambiguity and can adapt swiftly to changing priorities. What You'll Do - Lead the design, development, and deployment of Generative AI applications by immersing yourself in the needs of operational areas. - Build empathy for business challenges and apply engineering best practices to deliver innovative, robust, and reliable AI solutions. - Drive the end-to-end lifecycle of AI/ML projects, from ideation and experimentation through production deployment. - Use prompt engineering, data versioning, systematic experimentation, and A/B testing to optimize performance. - Develop and maintain secure, scalable infrastructure on AWS, focusing on cloud resource management, cost optimization, and compliance. - Prioritize security and fairness throughout the development and deployment process to protect our customers and uphold ethical standards. - Collaborate on systems design and integration, ensuring seamless interoperability between AI systems and both modern and legacy front-end/back-end platforms. - Maintain CI/CD pipelines and automated testing frameworks to support continuous delivery and operational reliability. - Partner with business and technical stakeholders to identify high-impact use cases for generative AI in underwriting, customer service, claims, and risk modeling. - Implement and promote MLOps best practices, enabling reproducibility, scalability, and model governance throughout the development lifecycle. - Lead delivery using agile methodologies, incorporating continuous feedback and fostering a culture of experimentation and iterative improvement. - Own your solutions from prototype through production. - Stay informed about emerging trends in Generative AI, cloud-native technologies, model governance, and responsible AI. - Mentor junior data scientists and engineers, promoting best practices in AI development, documentation, and cross-functional collaboration. Qualifications - Bachelor’s degree in Computer Science, Engineering, or analytical fields (Mathematics, Data Science, etc.), or equivalent experience. - At least 5 years deploying and supporting full-stack applications in enterprise environments. - Several years of experience integrating AI, MLOps into full-stack applications. - Experienced in designing and shipping production Generative AI-based applications with agentic architectures. - Strong skills in prompt engineering, orchestration frameworks (e.g., LangChain, LangGraph, or similar), tool/function calling, structured outputs, model selection, and LLM evaluation. - Experienced in full-stack development (backend, frontend, and database technologies) with expertise in technologies such as Vue and TKG. - Strong skills in Python, TypeScript, git, SQL, CI/CD pipelines, automated testing, and DevOps best practices. - Experienced in AWS services, particularly Bedrock, SageMaker, S3, Lambda, and infrastructure as code (CDK). - Extensive experience applying software engineering design patterns and enterprise application architecture principles. - Experienced in data science, machine learning techniques, and data engineering. - Skilled at addressing fairness in AI development and applying experimentation and A/B testing methodologies. - Strong communicator and collaborator, experienced at building partnerships in remote and ever-changing environments. - Resilient and resourceful problem solver, proactive at overcoming obstacles to achieve goals. - Comfortable with ambiguity and change, demonstrating high learning agility. Requirements - Advanced degree in an analytical field (preferred). - Familiarity with single or multi-cloud agentic architecture for building LLM-based applications (preferred). - Proven experience with MLOps and implementing automated pipelines for model and algorithm training, monitoring, versioning, deployment, and scaling in production environments (preferred). Benefits - $130,000 to $150,000, eligible for annual bonus, as applicable. - 401(k) plan with a 2% company contribution and 6% company match. - Work-life balance with vacation, personal time, and paid holidays.

• Transform data into powerful insights capable of driving enlightened decision-making • Work closely with the client to understand their business needs • Frame those needs as statistical problems and solve them with leading-edge methodologies • Collaborate on projects that help clients spin their data into a high-performance asset