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About This Role
From Fivetran's founding until now, our mission has remained the same: to make access to data as simple and reliable as electricity. With Fivetran, customer data arrives in their warehouses, canonical and ready to query, with no engineering or maintenance required. We're proud that more organizations continue to leverage our technology every day to become truly data\-driven.
About Us
Fivetran and dbt Labs are bringing together two industry\-leading companies with a shared mission: helping organizations unlock the full value of their data.
Together, we're delivering the data infrastructure layer that helps organizations move, transform, and trust their data — from the moment data moves, through every transformation, to the context teams and AI systems rely on.
Fivetran helps organizations automate data movement across the systems, clouds, engines, and tools they rely on. dbt Labs pioneered analytics engineering, helping teams transform data into reliable, governed insights. Together, we support thousands of organizations as they build a trusted foundation for analytics, AI, and better business decisions.
As we bring our teams and technology together, we're building on the strengths of both companies while continuing to deliver the products and experiences our customers know and trust. It's an exciting time to join us: we're creating a company with the scale, talent, and technology to help more organizations put their data to work with greater speed, confidence, and impact.
During this transition period, you may see references to both Fivetran and dbt Labs throughout our recruiting process as we integrate our teams, systems, and career sites.
About the Role
This is a unique role that sits at the intersection of economics, analytics, product, and strategy. You'll combine rigorous quantitative analysis with customer research and commercial thinking to shape how Fivetran prices, packages, and monetizes its products.
What You'll Do
- Analyze product usage, deal, financial, and billing data to model pricing, packaging, and monetization scenarios.
- Design frameworks to assess willingness to pay, customer value, and price realization across customer segments and product lines.
- Conduct research on pricing strategies, packaging models, and monetization approaches across comparable and adjacent markets.
- Lead pricing and packaging strategy for new product launches, partnering with Product, GTM, and Finance to define commercial models
- Build value\-based pricing and econometric models to quantify elasticity, customer ROI, and lifetime value.
- Design and execute pricing experiments
- Synthesize quantitative analysis, customer research, and competitive intelligence into executive recommendations that influence pricing strategy.
- Drive customer and prospect research to understand purchasing behavior, value perception, and pricing friction.
- Partner closely with Product, Analytics, Finance, Sales, Marketing, and Legal to deliver end\-to\-end pricing initiatives.
- Define KPIs and dashboards that measure pricing performance and identify optimization opportunities.
- Continuously evolve our pricing and packaging strategy to improve ARR growth, customer adoption, and value realization.
Skills We're Looking For
- 5–8 years designing or operating SaaS or sophisticated B2C pricing models, preferably with usage‑based or behavioral pricing experience
- Deep quantitative chops—an advanced degree (PhD/MSc) in Economics, Statistics, Operations Research, Applied Math, or similar
- Fluency in SQL and Python for large‑scale data analysis; proficiency with BI tools such as Looker or Tableau; bonus for dbt
- Hands‑on experience with conjoint, MaxDiff, Gabor\-Granger, or Van Westendorp surveys and causal experiment design
- Strong attention to detail and data analysis skills – including interpreting results and recommending actions
- Proven record of turning complex models and analyses into recommendations and shipping changes that moved ARR, gross margin, or NRR
- Collaborative mindset with Product, Analytics, Finance, Sales, and Legal; you can navigate from whiteboard to contract terms with ease
- Strong communication and presentation skills with ability to influence at the executive level
\#LI\-DJ1 \#LI\-Remote
Perks and Benefits
- 100% employer\-paid medical insurance\*
- Generous paid time\-off policy (PTO), plus paid sick time, inclusive parental leave policy, holidays, and volunteer days off
- RSU stock grants\*
- Professional development and training opportunities
- Company virtual happy hours, free food, and fun team\-building activities
- Monthly cell phone stipend
- Access to an innovative mental health support platform that offers personalized care and resources in areas such as: therapy, coaching, and self\-guided mindfulness exercises for all covered employees and their covered dependents.
- *May vary by country and worker type \- please reach out to your recruiter for more information*
*Click* *here* *to learn more about Fivetran's Benefits by Region.*
We're honored to be valued at over $5\.6 billion, but more importantly, we're proud of our core values of Get Stuck In, Do the Right Thing, and One Team, One Dream. Read about us in Forbes.
Fivetran brings together high\-quality talent across the globe to make data access as easy and reliable as electricity for our customers. We value and recognize that our customers benefit from having innovative teams made of people from many backgrounds, experiences, and identities. Fivetran promotes diversity, equity, inclusion \& belonging through attracting, recruiting, developing, and retaining a diverse workforce, not only because it is the right thing to do, but because it helps us build a world\-class company to better serve our customers, our people and our communities.
To learn more about Fivetran's culture and what it's like to be part of the team, click here and enjoy our video.
To learn more about our candidate privacy policy, you can read our statement here.
*We are committed to ensuring that all candidates have an equal opportunity to participate in our interview process. If you require accommodations at any stage of the process due to a disability, medical condition, or any other circumstance, please don't hesitate to submit your request by filling out this* *form**. We will work with you to provide reasonable accommodations to facilitate your participation and ensure a fair and accessible interview experience. Your request and any information provided will be kept confidential and will not impact your candidacy. We look forward to hearing from you and accommodating your needs to the best of our ability.*
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Fivetran, 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Fivetran AI Hiring
Fivetran has 4 open AI roles right now. They're hiring across AI Software Engineer, Data Scientist. Positions span New York, NY, US, Chicago, IL, US. Compensation range: $209K - $290K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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