Senior Product Analyst

Splash Financial•Cleveland, OH

20h•$150,000 - $180,000•Remote

About The Position

Splash Financial is looking for a Senior Product Analyst to help us answer the "why" behind customer behavior and product performance. You'll be the analytical lead of our product organization, owning the experimentation, instrumentation, and insight work that shapes our roadmap and the customer experience. In this role, you'll design and analyze A/B tests, turning results into clear, compelling stories for both executives and cross-functional partners. You'll lead our product analytics stack, define the metrics that matter, and build intuitive dashboards that give teams a trusted, self-serve view of product health. You'll also build a unified customer 360 view that connects acquisition, onboarding, servicing, and outcomes, giving Splash a holistic understanding of our borrowers. Beyond the data work, you'll own a lightweight research program — customer surveys, NPS analysis, and interview synthesis — ensuring product decisions are grounded in both quantitative rigor and qualitative insight. Sitting at the intersection of data, product, and customer experience, this role is a critical partner in driving growth and conversion across our portfolio.

Requirements

5-8+ years of experience in product analytics, data science, business intelligence, or a related field, preferably in fintech, consumer lending, or a consumer marketplace environment.
Strong proficiency in SQL and working with relational databases; comfort validating large datasets and joining complex tables. Experience with a scripting language (e.g., Python or R) for deeper analysis, modeling, or automation is a plus.
Demonstrated experience designing, running, and evaluating A/B tests (or similar experiments) — including hypothesis formation, sample size estimation, guardrails, and clear communication of results and trade-offs.
Hands-on experience with modern product analytics and BI tools (e.g., Logrocket, Amplitude, Mixpanel, Heap, Looker, Tableau, Mode, etc.), and a track record of building dashboards and self-serve tools that enable others to answer their own questions.
Familiarity with research methods- survey design, NPS analysis, usability study basics, or competitive intelligence and the ability to extract signal from qualitative data and pair it with quantitative analysis. Experience running or synthesizing customer interviews is a plus.
Working knowledge of AI/ML tools and how they apply to analytics workflows, including LLM-assisted data analysis, AI-powered product features, and the ability to instrument and measure AI-driven experiences. Comfort using AI tools to accelerate your own work is expected.
Entrepreneurial, resourceful, and comfortable operating with ambiguity; you identify opportunities, propose solutions, and drive them to completion with limited guidance while maintaining a high bar for quality.

Responsibilities

Own experimentation and test analytics: Design, implement, and analyze A/B and multivariate tests across the product experience, including funnel optimizations to new feature launches
Build and maintain core product & business dashboards: Develop, automate, and continuously improve dashboards and reporting that track key product, funnel, and customer metrics; make it easy for PMs and leaders to self-serve insights and monitor performance.
Lead product analytics tooling and instrumentation: Own the roadmap, governance, and best practices for our product analytics stack (event schemas, tracking plans, data quality); partner closely with Product and Engineering to ensure every new feature ships with the right instrumentation and metrics.
Create and evolve a customer 360 view: Bring together product usage, marketing, servicing data into a holistic view of the customer journey; identify segments, behaviors, and opportunities that drive acquisition, conversion, engagement, and long-term value.
Translate customer feedback & data into actionable insights: Combine quantitative analysis (funnels, experiments) with qualitative signals (surveys, NPS, interviews, customer service calls and tickets) to surface insights and recommendations that shape product strategy and prioritization.
Own lightweight customer research programs: Lead customer surveys, NPS programs, and usability interview syntheses; conduct competitive and market analysis; translate qualitative findings into product recommendations that complement quantitative work
Leverage AI tools to accelerate analysis and insight generation: Actively use AI-powered tools (e.g.- LLM-assisted data exploration, AI-enhanced analytics platforms) to increase the speed and depth of analysis; help the team develop frameworks for where AI can improve experimentation, personalization, and product decisions