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About This Role
Company Overview
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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
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Docusign's Product Data Science team is looking for a Data Scientist to support the development of key data products and capabilities to enable data\-driven product development and decision making. In this role, you would be part of the Product Data Science team and work directly in partnership with a variety of stakeholders including but not limited to Product, Engineering and User Experience partners to support and empower them with data\-driven actionable insights. You will be embedded in specific product spaces requiring you to establish and grow relationships. As a Data Scientist, you'll dig into the data to uncover insights, identify opportunities for product improvements and new product development, define product metrics with goals, design experiments and develop data science models to drive customer experience, engagement, and adoption of Docusign's products. If you are an experienced data scientist passionate about data\-driven product development and building products and capabilities that enable a culture of "test and learn," then this could be a great role for you.
This position is an individual contributor role reporting to the Vice President, Operations \& Analysis.
Responsibility
- Collaborate with cross\-functional teams to research, build and improve data analysis to identify opportunities for product improvements, new product features, product utilization, and improve customer experience, engagement, and retention
- Be the subject matter expert for driving the product data strategy for the areas you support
- Partner closely with the product managers, user researchers, engineers, and leadership to capture and prioritize potential insights, analysis, and data product opportunities that will drive maximum business impact
- Specify and help implement product telemetry, define key performance indicators with goals, and own and manage regular reports to create insights around product usage and success
- Partner with Global Data Analytics team to automate data pipelines to snowflake from different source systems
- Design and build automated dashboards that connect data from internal and external data sources, with visualizations and tools that allow business users to self\-serve on their website data needs
- Partner with Product teams to design, administer, and analyze the results of A/B and multivariate tests and other ML models
- Leverage data to develop actionable analytical insights and present findings to senior management
- Evangelize models, frameworks, analysis, and insights with stakeholders and business partners
- Act with a sense of urgency and purpose, identify and resolve roadblocks, reach out to cross\-functional team members to solicit input and/or assist when appropriate
Job Designation
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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
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Basic
- BA/BS degree in a quantitative field (e.g., Statistics, Math, CS, Economics or Finance) or equivalent work experience
- 5\+ years of demonstrated experience with business analytics or product analytics in a data science or analytics role in a SaaS/Cloud industry
- Experience pulling data from external sources, joining data from disparate data systems and distilling large data sets into actionable insights that drive business value
- Experience with SQL for data analysis and data validation
- Experience with data visualization tools such as Tableau, Power BI, Hex or similar (experience \= design, create data feeds and execute on usable dashboard to address required business questions)
- Experience with statistics and rigorous analytical techniques
- Experience solving real business problems using data and analytics
- Experience communicating data and technical work to non\-technical stakeholders
- Experience working with Product Engineering and other technical teams to implement the product telemetry requirements
- Experience with agile/scrum product development
Preferred
- Strong communication and leadership skills, especially the ability to collaborate cross\-functionally and lead through influence across functional and organizational lines
- Ability to present data in a visual format and explain the insights clearly and concisely
- Experience with A/B testing, cohort analysis, user segmentation, and other core product analytics methodologies
- Experience designing and refining machine learning models
- Experience with Python or R
- Familiarity with data engineering processes
- Highly motivated, organized, self\-starter who thrives in a fast\-paced environment
- A strong individual contributor who partners well with internal teams
Wage Transparency
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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:
Washington, Maryland, New Jersey and New York (including NYC metro area): $126,500\.00 \- $182,500\.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
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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. Accommodation
Docusign is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need such an accommodation, or a religious accommodation, during the application process, please contact us at [email protected].
If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at [email protected] for assistance.
Salary Context
This $126K-$182K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At DocuSign, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($154K) sits 22% below the category median. Disclosed range: $126K to $182K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
DocuSign AI Hiring
DocuSign has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Seattle, WA, US, Washington, DC, US. Compensation range: $182K - $232K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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