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United States
Remote only
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Scientist - Growth & Expansion based in the United States.
The Staff Data Scientist will own the customer-growth analytics agenda across the full lifecycle, from onboarding and activation through retention, multi-product adoption, and expansion.
You’ll connect product behavior, go-to-market activity, and financial targets to create a shared evidence base for growth decisions.
The role is highly cross-functional, partnering with Growth, GTM, Finance, Product, Analytics Engineering, and Data Science teams.
You’ll identify the behaviors and customer segments that predict long-term expansion and translate those insights into actionable investment priorities.
As a Staff-level individual contributor, you’ll set analytical standards, shape measurement frameworks, and influence senior leaders without relying on direct authority.
You’ll work with governed data in an evolving analytics environment while helping strengthen the foundations needed for scalable growth measurement.
This is a hands-on, direction-setting opportunity with potential to build and lead a dedicated analytics pod as the mandate expands.
Own the customer-growth outcome end to end, covering onboarding, activation, trial-to-paid conversion, multi-product adoption, retention, and expansion revenue.
Establish clear, trusted metrics for each stage of the customer lifecycle and ensure Growth, GTM, Finance, and Product teams work from a consistent evidence base.
Identify early- and mid-lifecycle behaviors that predict long-term expansion, including second-product adoption, higher-tier plans, and add-on attachment.
Translate behavioral and revenue data into segment-level insights that shape product priorities, growth initiatives, marketing investment, and go-to-market decisions.
Model the B2B2C customer journey, including agency signup, sub-account activation, adoption, and expansion, to identify where customer value compounds or is lost.
Partner with experimentation and causal-inference specialists to design and evaluate growth experiments rigorously, distinguishing causal effects from correlation.
Collaborate with Product Strategy, Growth, and other teams on trial-to-paid conversion, churn, and add-on initiatives to establish shared definitions and analytical models.
Set the technical direction for company-wide customer-growth measurement, including canonical metrics, segment definitions, and customer-value models using governed and certified data.
Develop reusable frameworks and methodologies for growth measurement and causal inference that Analytics Engineering and adjacent Data Science teams can adopt.
Serve as a trusted analytical advisor to Growth, GTM, Finance, and Product leaders, translating complex analysis into clear, decision-ready recommendations.
Influence roadmaps, investment decisions, and growth strategies through rigorous analysis without directly owning those functions.
Establish analytical standards for adjacent Data Science and Analytics teams, raising the quality and consistency of growth measurement across the organization.
Identify data gaps and collaborate with Analytics Engineering to improve the event taxonomy and data foundation required for funnel and customer-value analysis.
Use AI-assisted tools to accelerate exploration, documentation, analysis, and other analytical workflows.
Help establish reusable patterns and scalable foundations that can support the customer-growth mandate as the organization grows.
9+ years of experience in product or growth analytics, data science, applied statistics, or a closely related discipline, with substantial experience across activation, retention, conversion, and expansion.
Demonstrated experience building a metric, measurement, or customer-value framework that has been adopted by multiple teams and used to drive measurable business outcomes.
Strong applied statistics expertise, including the ability to frame analyses around causal questions and recognize common limitations and failure modes of correlational analysis.
Practical experience partnering on experiments, including A/B test design, statistical power, guardrails, and rigorous interpretation of results.
Strong SQL skills and working proficiency in Python.
Experience working in a modern data environment involving Snowflake, dbt, or comparable technologies.
Proven ability to turn behavioral, product, and revenue data into segment-level insights that influence product, growth, or go-to-market decisions.
Comfortable working with imperfect or evolving data environments, using governed sources while helping identify opportunities to strengthen the underlying foundation.
Strong cross-functional influence and communication skills, with the ability to align Growth, GTM, Finance, Product, and Data teams around shared metrics and conclusions without direct authority.
Ability to communicate sophisticated analytical findings clearly to both technical and executive audiences.
Highly strategic, intellectually curious, pragmatic, and comfortable operating with broad ownership and ambiguity.
Experience with B2B SaaS, CRM, or product-led growth models is a plus, particularly freemium, trial, land-and-expand, or similar motions.
Exposure to usage-based, consumption-based, or add-on revenue models is advantageous.
Familiarity with Statsig or a comparable experimentation platform is a plus.
Experience with AI-assisted analytics workflows and/or mentoring analysts is desirable.
Annual salary range of $163,400–$220,000, depending on experience and role-related qualifications.
Fully remote work environment within the United States.
Staff-level scope with significant ownership of a company-wide customer-growth analytics mandate.
Opportunity to work cross-functionally with Growth, GTM, Finance, Product, Analytics Engineering, and Data Science teams.
High degree of autonomy and influence over analytical standards, measurement frameworks, and growth strategy.
Opportunity to advise senior leaders and directly influence product roadmaps, investment decisions, and go-to-market priorities.
Opportunity to develop reusable analytical and causal-inference frameworks that can scale across teams.
Potential to grow a dedicated analytics pod as the customer-growth mandate expands.
Exposure to a large-scale data environment supporting millions of businesses, substantial product usage, and complex customer journeys.
Opportunity to leverage AI-assisted tools and modern data practices to accelerate analytical work and decision-making.
Equal opportunity employment environment with voluntary demographic information collected separately for applicable compliance and reporting purposes.
