Skip to content

GTM Analytics Engineer

187k – 230kSan Francisco, CANew York, NYHybrid7+ YOE
Summary

Lead Growth Data Science efforts at Gusto: drive experimentation, causal analysis, and metric design to optimize customer acquisition, retention, and expansion. Mentor junior scientists and partner with Revenue, Product, and Finance leaders.

About the role

Responsibilities

  • Shape the analytical strategy for Growth product initiatives; identify high-leverage opportunities, set long-term measurement and experimentation direction, and align executive stakeholders
  • Drive and execute on org-level analytical roadmaps that shape company strategy, leveraging AI tools and creating efficiencies through AI-native approaches
  • Develop frameworks for experimentation, causal analysis, and metric design that scale across multiple teams
  • Serve as a trusted advisor to senior product, engineering, and Revenue business leaders; anticipate emerging questions and proactively define success measures
  • Push the boundaries of statistical modeling, experimentation, and AI-assisted analytics; design methods and tools that expand how Gusto leverages data
  • Mentor and coach more junior data scientists, raising the bar for analytical thinking and storytelling

Requirements

  • 7–10+ years of experience in Data Science at a product-focused software company
  • Strong SQL and Python skills
  • Proven ability to apply statistical methods, causal inference, and experimental design to real business problems
  • Demonstrated experience leveraging AI tools (e.g., Claude) and executing work in an AI-native way
  • Excellent communication skills with a track record of influencing cross-functional stakeholders and leadership
  • Demonstrated experience leading large, technically complex projects with clear business impact
  • Proactive, resilient problem-solver who independently structures ambiguous problems into actionable insights
  • Passion for mentoring others and raising the bar for data science craft
  • BS/MS/PhD in a quantitative field (Statistics, Economics, Computer Science, Applied Math, etc.) or equivalent industry experience

Nice-to-Haves

  • Experience developing Machine Learning models

Compensation & Benefits

  • Cash compensation targeted at $187,000–$230,000 in New York & San Francisco Bay Area
  • Stock equity (RSUs) in addition to base pay
  • Competitive benefits package for full-time employees
Skills
SQLPythonStatistical ModelingCausal InferenceExperimental DesignMachine LearningAI ToolsData StorytellingMentorship
Similar roles at this salary range
All Data Science jobs →
Fetch

Senior Data Scientist

Lead high-impact data science initiatives at Fetch, designing predictive and causal models, building experimentation frameworks, and partnering cross-functionally to drive personalization, retention, and monetization strategies.

184k – 216kUnited StatesData ScienceRemote8+ YOESQLdbt
Customer.io

Senior Manager, Data Science & Analytics

Lead and grow a data science & analytics team that partners with Product, Marketing, Sales, and CX to drive forecasting, experimentation, behavioral analytics, and measurement at a B2B SaaS company.

170k – 200kUnited StatesData ScienceRemote7+ YOESQLdbt
Chime

Director, Marketing Data Science

Lead and scale Chime's Marketing Data Science function, setting vision and strategy for marketing analytics that drives business outcomes. Manage a team of analysts using MMM, experimentation, and causal inference to inform marketing investment decisions.

217k – 300kSan Francisco, CAData ScienceHybrid10+ YOESQLPython
Pinterest

Staff Data Scientist, Ads Product

Staff Data Scientist driving ads product strategy and data-driven decision-making at Pinterest. Partners with product, engineering, and finance teams to translate business questions into analytical solutions and maintain data quality.

165k – 339kSan Francisco, CAData ScienceRemote8+ YOESQLPython
Sift

Staff Data Scientist

Staff Data Scientist owning advanced ML modeling strategies for fraud detection across payment fraud, account takeover, and identity abuse. Requires 5+ years production modeling experience, deep fraud/security domain expertise, and mastery of tree-based, deep learning, and graph methods.

195k – 265kUnited StatesData ScienceRemote5+ YOECNNsRNNs