Bloomerang is the Giving Platform built for purpose, trusted by 24,000+ nonprofits to raise more funds, retain more supporters, and create lasting change. By unifying fundraising, CRM, and volunteer management in one easy-to-use platform, Bloomerang gives organizations a complete view of every supporter and the tools to build stronger relationships. Backed by expert support and a team passionate about nonprofit success since 2012, Bloomerang is more than software—it’s a growth partner for missions that matter.

The Role:

As a Product Analyst at Bloomerang, you aren’t just transitioning into the field; you’re an experienced Analyst in the SaaS space. You’ve built the cohort studies, churn models, and A/B frameworks that Product Managers actually use to move the needle. You don't just take tickets—you manage your own backlog, select your own methodologies, and act as the proactive bridge between Product, Engineering, and Marketing. In an era where AI has democratized data exploration, your value is sharper than ever. You leverage AI to scale output and accelerate pattern recognition, but you provide the statistical rigor and human judgment those tools lack. You are the gatekeeper of truth—ensuring every insight is stress-tested, every experiment is designed correctly, and every piece of data reaching leadership reflects reality.

What You Will Do:

Lifecycle Monitoring & Experimentation: Track and analyze core product metrics — usage, retention, activation, and revenue signals — to proactively surface trends and friction points before they reach stakeholder escalations. Design, execute, and analyze A/B tests and cohort studies, translating results into clear, evidence-backed product recommendations rather than inconclusive reports.

Predictive & Diagnostic Analytics: Apply regression analysis, significance testing, and time-series forecasting to go beyond descriptive reporting and into diagnostic and predictive territory — answering not just what happened, but why, and what’s likely next. Model forward-looking product trends including churn risk and customer lifetime value, with a standard for methodological rigor that makes results defensible to senior leadership.

User Sentiment & Competitive Intelligence: Synthesize qualitative signals — user interviews, NPS, support data, polls — with quantitative usage metrics to build a full-spectrum view of the customer experience, not just a metrics dashboard. Conduct competitive benchmarking to evaluate Bloomerang’s product positioning, identify feature gaps, and surface pricing and packaging opportunities.

Measurement Ownership & Data Integrity: Define measurement requirements for new features in partnership with Product — ensuring that success criteria, instrumentation, and baseline metrics are established before development begins, not retrofitted after launch. Maintain accountability for data quality and consistency across product datasets, proactively investigating upstream gaps and anomalies before they corrupt downstream conclusions.

AI-Augmented Analysis: Use AI assistants and LLMs as active tools in your analytical workflow to automate exploratory analysis, accelerate investigation, debug SQL logic, and surface non-obvious patterns at scale. Share AI prompts, reusable SQL snippets, and analytical templates with the broader team to multiply collective output — and apply critical judgment to AI-generated results to ensure they meet the same methodological bar as any other analysis.

Stakeholder Storytelling & Influence: Transform complex analytical findings into decision-ready narratives — visual, written, and verbal — that move product managers and cross-functional leads from curiosity to action. Practice guided persuasion: use data storytelling and curious questioning to align partners toward shared conclusions, build data literacy across teams, and influence product direction through evidence.

What You Need to Succeed:

Analytical Experience: 3–5 years of hands-on experience working directly as a Product Analyst in a SaaS or technology company.
Technical Proficiency: Expert proficiency in SQL for complex data wrangling against large datasets (BigQuery preferred).

Demonstrated ability to apply advanced statistical methods — regression, significance testing, time-series analysis — and to explain your methodology choices to technical and non-technical audiences.

Experimentation & Insight Generation: Proven experience analyzing A/B tests and cohort studies, translating statistically sound results into clear product recommendations. Track record of synthesizing qualitative and quantitative data sources into unified, compelling narratives.

Tools & AI Fluency: Proficiency with Business Intelligence and data visualization tools; Tableau and/or Looker experience preferred. Demonstrated fluency with AI-assisted analytical tools — LLMs, AI copilots, automation — as genuine productivity multipliers, with the critical judgment to validate and interrogate what’s produced.

Nice to Haves But Not Required:

Hands-on experience prompting LLMs and AI tools for analytical tasks — writing queries, debugging logic, automating EDA, or generating data narratives.

Familiarity with data transformation tools such as DBT.

Experience with product event tracking and instrumentation tools (e.g., Pendo, Google Analytics, Segment).

Background in predictive modeling or exposure to ML frameworks beyond standard regression.

Experience in the nonprofit sector, fundraising technology, or donor management platforms.: