Product Analyst
6 days ago
We are looking for a highly analytical and curious Product Analyst to join our team. This role will work closely with Operations, Product Management, and Customer Success to generate actionable insights, optimize product performance, and enhance customer outcomes.
You will manage A/B tests, conduct coverage and usage analysis, and develop data-driven recommendations that improve product capabilities, customer experience, and business growth.
Key Responsibilities
- Partner with Product, Ops, and CS to translate business questions into data analyses and actionable insights.
- Design, run, and evaluate A/B tests to assess system performance and product features.
- Perform coverage analysis (e.g., which areas, features, or customer needs are sufficiently addressed by the product).
- Conduct usage analysis to identify adoption patterns, friction points, and opportunities to improve engagement.
- Build dashboards, reports, and KPIs to track product health and customer success impact.
- Provide insights that help CS teams better support enterprise customers and demonstrate product value.
- Collaborate with Product to prioritize improvements and influence the roadmap.
- Develop playbooks and best practices for ongoing product performance monitoring.
Requirements
- 3-5 years of experience
as a Product Analyst, Data Analyst, or similar role in SaaS or enterprise tech. - Experience designing and evaluating
A/B tests and experiments. - Strong SQL and data analysis skills; comfort with BI tools (Looker, Tableau, Power BI, or similar).
- Proficiency in Python (pandas, NumPy, etc.) for data analysis and automation.
- Strong business acumen and ability to work cross-functionally.
- Excellent communication skills, with the ability to translate complex analyses into clear recommendations.
- Self-starter who thrives in a fast-paced, high-growth environment.
Nice to Have
- Experience working with customer success or B2B enterprise SaaS.
- Knowledge of product analytics platforms (Amplitude, Mixpanel, Pendo).
- Background in statistics or data science.
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