Principal Data Scientist, Analytics (AI)

Location: Hybrid in San Francisco, New York, or Seattle, United States

Annual base compensation: $193,500–$227,500

Company

hirify.global develops data collaboration solutions that help brands, retailers, financial services providers, and healthcare innovators use data responsibly for marketing and customer insights.

What you will do

• Design and build reusable self-service metrics, dashboards, analytical products, skills, and AI agents for Product and Engineering stakeholders.
• Create semantic layers, metric frameworks, analytical abstractions, and natural-language querying capabilities.
• Develop predictive, diagnostic, causal, scenario, and simulation models for product adoption, engagement, retention, and monetization.
• Lead experimentation strategies including A/B testing, quasi-experiments, and causal inference.
• Partner with architects, data engineers, Product, Engineering, and Design to build scalable data models, feature pipelines, and reproducible data science workflows.
• Mentor analysts and data scientists, lead cross-functional analytics projects, and present high-impact findings to leadership.

Requirements

• MS or PhD in Computer Science, Statistics, Mathematics, or a related field.
• 10+ years of experience in Data Science and Analytics delivering product insights and statistical models at scale.
• Expert-level Python and SQL skills, experience with massive cloud datasets such as BigQuery, and hands-on experience with complex data science and LLM models.
• Experience building and scaling AI-powered analytics and architecting a modern data science stack.
• Deep understanding of product analytics metrics, preferably in a SaaS or platform environment.
• Advanced experience with data engineering partnerships and dbt or similar data modeling frameworks, plus strong analytical rigor and reproducibility practices.

Culture & Benefits

• Hybrid work environment across San Francisco, New York, and Seattle.
• Collaboration with Product, Engineering, Design, and data engineering teams.
• Opportunity to shape AI-driven analytics governance, explainability, and best practices across the organization.
• Compensation is adjusted based on experience, skills, geography, and internal equity.