Senior Product Manager, AI & Data Science Products

Location: Fully remote

Company

hirify.global provides predictive intelligence on private companies using live private company data, AI, and market activity.

What you will do

• Own the strategy and roadmap for hirify.global’s customer-facing AI data layer.
• Identify, test, and validate predictions, classifications, signals, and insights that improve customer workflows and decisions.
• Take successful AI data concepts from experimentation through scaled adoption across products, APIs, MCP, and data delivery experiences.
• Partner with Design, Engineering, Data Science, Go-to-Market, Pricing and Packaging, and Sales on product delivery, launches, and monetization.
• Define quality standards and evaluation frameworks for model-derived data, including confidence and uncertainty.
• Monitor adoption, retention, expansion, revenue, customer outcomes, and data performance to improve or retire products.

Requirements

• 3+ years of Product Management, Data Product Management, AI/ML Product Management, or comparable experience.
• Experience owning customer-facing data science products from problem definition through launch and ongoing monitoring.
• Strong understanding of data products, customer discovery, product strategy, prioritization, experimentation, and tradeoffs.
• Practical understanding of modern AI and machine learning capabilities, limitations, and applied data science.
• Experience partnering with Data Science and Engineering teams and translating product requirements for technical teams.
• Ability to evaluate probabilistic data using concepts such as precision, recall, confidence, model drift, quality, coverage, cost, and speed.

Nice to have

• Experience with B2B SaaS, data products, APIs, intelligence platforms, or commercializing differentiated data.

Culture & Benefits

• Fully remote work environment.
• Inclusive culture focused on transparency, openness, and diverse perspectives.
• Structured interview process with consistent, role-relevant evaluation.

Hiring process

• Recruiter prescreen covering role basics, motivation, logistics, compensation alignment, and priorities.
• Hiring-manager evidence interview followed by a work sample or functional deep dive.
• Final decision-gap interview focused on unresolved evidence such as collaboration, judgment, leadership, or values in practice.