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ShipIn is an AI-powered visual fleet intelligence platform serving the maritime industry. The company helps shipowners and operators reduce operational risk, improve safety, and enhance decision-making by connecting onboard video with shore-based teams and converting vessel operations into actionable insights.
As ShipIn's first Product Analyst, you will be the central data driver for product decisions. Reporting to the VP of Product, you'll combine hands-on analytics with strategic research to validate product performance and discover new opportunities in maritime and insurance domains.
Key responsibilities include:
- Serve as the primary owner of product data analysis, validation, and reporting
- Analyze user engagement across onboard and shore-based personas to understand adoption, workflows, and value extraction
- Build dashboards and KPI tracking systems for risk scores and product usage across customers and fleets
- Partner with Product Managers, Designers, and Engineers to identify bottlenecks, validate hypotheses, and optimize performance
- Maintain data quality and consistency across analytics sources
- Conduct data-driven research on emerging product opportunities, identifying trends and correlations in risk scores for maritime insurance
- Recommend new modules, features, or risk engineering tools aligned with maritime industry needs
You'll work cross-functionally with Data Science, Maritime experts, Insurance partners, and Customer Success teams to translate insights into clear product strategies.
Required qualifications: 4–6 years in product analytics, BI, or data research roles (SaaS, AI/ML-driven products, or data-rich enterprise applications). Hands-on SQL expertise and proficiency with analytics tools (Looker, Mixpanel, Amplitude, Tableau, or similar). Comfort with Python or R for deeper analysis. Strong ability to design and track KPIs, funnels, and cohort analyses. Bachelor's degree in a quantitative field (Data Science, Statistics, Engineering, Economics, or similar). Excellent communication skills to tell data stories for both technical and business audiences. Proactive, detail-oriented, and structured mindset.
Nice-to-have: familiarity with maritime, industrial, banking risk management, or insurance industries; understanding of AI/ML concepts and risk scoring frameworks.
