Lead Data Scientist, Product (AI)

TL;DR

Lead Data Scientist, Product (AI): Shape growth strategy and experimentation for an AI-powered audio/video creation platform with an accent on product PLG motion, B2B strategy, and self-service analytics. Focus on building reliable success metrics, opportunity sizing, and designing/analysing A/B tests to drive activation, engagement, and monetization.

Location: San Francisco, CA | Remote, US

Salary: $200,000 - $250,000/year

hirify.globalion">Company

hirify.global builds an AI-powered audio and video editing tool for fast recording, transcription, editing, and mixing.

What you will do

• Conduct in-depth analysis to shape product PLG motion and B2B strategy.
• Identify opportunities to drive activation, engagement, and monetization using actionable product insights.
• Build and maintain self-service analytics using AI tools so stakeholders can access reliable metrics, size opportunities, design experiments, and interpret results independently.
• Own success metrics and opportunity sizing to improve execution rigor across product teams.
• Partner with product and engineering teams on day-to-day experiment design, deployment, and analysis, including creative solutions for tricky experimentation scenarios.
• Evolve experimentation culture and practices across the company.

Requirements

• 5–8 years of experience as a Data Scientist or similar role.
• 3+ years working in a growth function.
• Strong statistical analysis skills and experience with A/B tests.
• Strong SQL skills and proficiency in at least one programming language such as Python or R.
• Strong business and product intuition for identifying growth opportunities.
• Excellent communication and collaboration skills.

Culture & Benefits

• Generous healthcare package.
• 401k matching program.
• Catered lunches.
• Flexible vacation time.
• Remote roles with periodic in-person collaboration opportunities; hybrid roles are flexible with no expectation of daily office attendance.

Hiring process

• Interviews to evaluate product/growth analytics, experimentation approach, and collaboration style.
• Discussion of role scope, success metrics ownership, and how experimentation culture will be scaled.