Product Analyst (AI)

Location: Munich, Germany; hybrid

Company

hirify.global operates a digital marketplace connecting buyers with manufacturers and providing access to global manufacturing capacity.

What you will do

• Embed within a product squad and provide analytical support for customer-facing and partner-facing marketplace platforms.
• Define product problems, hypotheses, success metrics, and experiments with Product Managers and stakeholders.
• Design, own, and analyze A/B tests for product features and pricing strategies to improve conversion, bookings, and revenue.
• Analyze product funnels, user behavior, activation, conversion, retention, pricing effects, lifecycle KPIs, and customer feedback.
• Build and maintain clear, reliable, and usable Looker dashboards and analytics tools.
• Specify data needs, validate data quality, and collaborate with the data warehouse team on marts and metrics.

Requirements

• Bachelor’s or Master’s degree in Data Science, Computer Science, IT, Engineering, or a related field.
• 3+ years of experience in product analytics, ideally in B2B or B2C marketplaces.
• Strong SQL and Python skills, plus experience with BI tools such as Looker, Tableau, or Qlik.
• Knowledge of probability, statistics, hypothesis testing, A/B test design, power analysis, regression, and time-series analysis.
• Experience with Git and project or process management tools such as JIRA and Confluence or equivalents.
• Fluent English with strong communication skills is required; German or another EU language is a plus.

Nice to have

• Understanding of data warehouse design, data structures, and ETL/ELT.
• Experience with Statsig or another A/B testing platform.
• Self-directed development in product, UX, machine learning, entrepreneurship, business administration, or human behavior and psychology.
• Russian language skills.

Culture & Benefits

• Cross-regional scope and close collaboration with Product Managers, stakeholders, and data warehouse specialists.
• Analytics is treated as a product, with emphasis on usability, adoption, data quality, and decision support.
• Technology stack includes SQL, Python, Snowflake, Airflow, Looker, Amplitude, Google Analytics, Git, Statsig, DBT, PostgreSQL, Ruby, Salesforce, and AI-native tools.