Senior Data Scientist, Business Analytics (AI)

Location: Remote; US time zones preferred

Company

hirify.global is a fast-moving consumer AI startup building privacy-first AI products on a permissive foundation focused on user sovereignty and practical everyday use.

What you will do

• Own data end to end, including data sourcing, storage, flows, access, querying, infrastructure, tooling, and dashboards.
• Consolidate financial, product, and growth data into a shared data lake with specialized analytical views.
• Build experiment-tracking systems that enable Product to monitor tests without spreadsheet-based workflows.
• Develop dashboards, self-serve analysis tools, data models, and reporting pipelines for decision-makers.
• Partner with Engineering, Product, Finance, Marketing, and leadership on instrumentation, business metrics, growth attribution, and strategic questions.
• Design privacy-conscious analytical methods and robust metrics for a rapidly changing product with limited user data.

Requirements

• 7+ years of demonstrated experience in data science, analytics, or a related field.
• Product-minded leadership with the ability to translate business questions into rigorous analysis and recommendations.
• Production-quality coding skills and fluency in SQL.
• Experience building data pipelines, models, analytical tooling, and modern data warehouse systems.
• Experience with PostHog for product analytics and Postgres for data storage and querying.
• Ability to communicate clearly with executives and explain uncertainty, confidence levels, and analytical limitations.

Nice to have

• Familiarity with more than one of Rust, TypeScript, Go, and Python.
• Experience using PostHog across error tracking, support, and conversation data.
• Experience with quasi-experiments, proxy metrics, qualitative triangulation, and decision frameworks for incomplete data.

Culture & Benefits

• Fast-moving startup environment with high ownership and direct impact.
• Privacy, free speech, user sovereignty, ethical data collection, and user trust are core principles.
• Collaborative work with Product, Engineering, Finance, Marketing, and executive leadership.
• Use of AI agents and LLMs to accelerate analysis, automate repetitive data work, and surface insights.
• Preference for timely, iterative analysis over delayed perfection.