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Monarch Money is a top-recommended personal finance platform launched in 2021, designed to simplify personal finance management. The company is fully remote and led by experienced entrepreneurs with a strong focus on AI-driven product development and data-driven decision-making.
As a Senior Product Analyst, you will own how Monarch measures product performance and learns from user behavior. You'll be the keeper of experimental rigor, investigating what drives conversion and retention, and running the experimentation loop end-to-end. This is a high-impact role that directly influences product strategy and business outcomes.
Key responsibilities include:
- Owning A/B test design, analysis, and partnering with product managers on conclusions and recommendations
- Working with the growth team across the full experimentation lifecycle: ideation, opportunity sizing, experiment design, analysis, and learning integration
- Analyzing user behavior and investigating anomalies to understand business changes
- Partnering with product and engineering on feature launches, defining measurement approaches and success metrics
- Providing data-driven insights to inform business decisions and sharing results with leadership
- Automating recurring workflows while maintaining insight quality
- Building new datasets and pipelines while maintaining existing data infrastructure
You'll work closely with peers on the data team and collaborate with growth, product, and engineering teams. The company emphasizes synchronous collaboration from 9 AM–2 PM PT with asynchronous work to accommodate time zones.
Requirements:
- Minimum 4 years in a product analyst role or similar, preferably at a growth-stage company
- Advanced knowledge of statistical methodologies for A/B testing, including experiment design and analysis
- Natural curiosity and comfort with ambiguity; ability to balance speed vs. accuracy in problem-solving
- Strong communication skills with both technical and non-technical partners; ability to translate requirements into actionable steps
- Expert-level SQL proficiency: comfortable with data selection, creating tables, advanced features (window functions), and writing clear, concise code
- Experience with modern analytics stack: Amplitude and Statsig for product/experimentation; Snowflake, DBT, Hex, and Omni for BI
- AI fluency: actively pushing AI boundaries and knowing when to use it and when it might introduce errors
Nice-to-haves:
- Experience in consumer subscription analytics
- Experience in early-stage, high-growth startups
