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About This Role
Sr. Data Scientist
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The team \+ the role
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Pendo's GTM Intelligence Team turns data into measurable outcomes across Sales, Marketing, and Customer Engineering. We combine analysis, ML models, and internal tooling to answer high\-value business questions and help GTM teams work faster and more effectively. We measure success by the real business value our work creates.
As a Senior Data Scientist, you'll own the full lifecycle of intelligence solutions, from problem definition and analysis through model development, stakeholder enablement, and ongoing iteration. You'll work directly with GTM teams to surface high\-value business problems and answer them with the right mix of analytics and modeling, translating what you find into decisions that stick. The best person for this role has strong modeling instincts, genuine curiosity about how GTM businesses operate, and the judgment to know when a complex model is the right tool — and when a well\-framed SQL query gets you there faster.
This role is based in Raleigh, NC and follows Pendo's hybrid model: in\-office 3 days per week.
### What this looks like day\-to\-day
- Leverage data analysis, machine learning, and predictive modeling to identify opportunities and mitigate risks for our GTM teams, from problem framing through delivery and ongoing iteration
- Work collaboratively with data \& AI engineers, analysts, revenue operations, and GTM stakeholders to ensure your work is actionable, interpretable, and clearly connected to business decisions
- Translate model outputs and analytical findings into clear business narratives through slides, write\-ups, presentations, and async video
- Leverage AI\-assisted development tools (Cursor, Claude Code) to accelerate delivery and prototype faster, while applying the critical thinking to validate, refine, and own the output
- Share and build reusable patterns, model documentation, and technical findings with the broader team
- Answer high\-value business questions through analysis and experimentation: develop hypotheses, own the approach, and communicate findings clearly to both technical and non\-technical audiences
- Partner with GTM teams to enable adoption of the models and tools you build, making sure they know how to use them and realize the value
Who you are
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Beyond the qualifications, we hire through a specific lens. These are the things we'll actually look for in how you talk about your work.
You connect data to the business.
You've worked alongside revenue teams enough to understand what drives their decisions. You know an insight nobody uses isn't worth much, so you think about adoption as much as performance. You can take something technical and explain it in a way that makes sense to the people who need to act on it.
You thrive in fast\-moving, ambiguous environments.
You know when a problem needs a thorough solution and when it just needs a good answer quickly. You're comfortable making a call with incomplete information and refining it as you learn more.
You're curious in a way that keeps you sharp.
The tools and techniques in this space change quickly, and you like to stay ahead of that. You don't usually wait to be told about something new; you're already trying it out for yourself. You've figured out how to make the most of emerging tools, including AI assistants, without handing your judgment over to them.
You make the people around you better.
You share what you learn and document what you build to make those around you better. You're willing to learn from others and make adjustments to make yourself better.
### Must\-haves
- 2\-4 years of experience in data science, analytics, or a related quantitative field
- Bachelor's or master's education in STEM (science, technology, engineering, math) field
- Experience using statistical computer languages (Python, SQL) to manipulate data and draw insights from large data sets
- Solid grasp of statistical concepts, data analysis, predictive modeling, and machine learning.
- Team oriented and highly agile thinker
- Self\-motivated with the ability to work independently and manage multiple priorities concurrently
- Clear communication skills to present complex analyses in a digestible format for diverse audiences
- A true team player with a collaborative, positive approach to solving challenges and supporting colleagues
- Experience with recurring revenue\-based business models preferred
- Direct experience working in or alongside a GTM function, such as Sales ops, Marketing analytics, Customer Success, or RevOps
- Comfort navigating ambiguity and moving fast; you can make a good decision with imperfect information and adjust as you learn more
### Nice\-to\-haves
- Experience with LLM\-based tooling, prompt engineering, or agentic AI workflows
- Familiarity with BI tools (Omni, Looker, Tableau) for self\-serve analytics delivery
- Prior experience working on a small, high\-ownership team where scope is broad and expectations are high
- Comfort quantifying the business impact of your work — you can tie model outputs to pipeline, retention, or efficiency metrics, not just model performance
- Background at a B2B SaaS company, ideally with exposure to product analytics or customer health data
About Pendo
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Pendo was founded in 2013 by former product managers, who combined their heads and hearts to build something they wanted but never had as product managers: a simple way to understand and attack what truly drives product success. Our mission is to improve society's experience with software. Come join one of the fastest\-growing startups, supported by best\-in\-class institutions like Battery Ventures, Salesforce Ventures, Spark Capital and Meritech.
Pendo core values: Bias to Act, Hone Your Craft, The Team is Pendo, and Maniacal Focus.
Location: Pendo is a hybrid culture. In\-office 3 days per week unless designated remote.
Benefits: Highly competitive, employer\-heavy coverage (including $0 premium options), strong 401(k) match, equity, and flexible time off.
Compensation: Our salary ranges are based on paying competitively for our size and industry, and are one part of many compensation, benefits and other reward opportunities we provide.
The expected salary range for this role to be performed in North Carolina is USD$133,000 – USD$160,000
EEOC: We are an equal opportunity employer and believe having diverse teams where everyone brings their whole self to Pendo is key to our success. We welcome all people of different backgrounds, experiences, abilities and perspectives.
Accessibility: Pendo is committed to working with, and providing access and reasonable accommodation to, applicants with mental and/or physical disabilities. If you think you may require an accommodation for any part of the recruitment process, please send a request to: [email protected]. All requests for accommodations are treated discreetly and confidentially, as practical and permitted by law.
Salary Context
This $133K-$160K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Pendo, 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($146K) sits 24% below the category median. Disclosed range: $133K to $160K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Pendo AI Hiring
Pendo has 1 open AI role right now. They're hiring across Data Scientist. Based in Raleigh, NC, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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