[Zilch] Senior Product Analyst

Zilch is a payment tech company founded in 2018, with over 5 million customers and double unicorn status. The company focuses on creating a payment platform that combines debit, credit, and savings allowing customers to earn cashback or spread costs interest-free.

• 4–6 years of experience in product analytics, payments analytics, marketplace analytics, or a related quantitative role.
• Strong curiosity to investigate anomalies and generate insights.
• Advanced skills in SQL and data manipulation.
• Solid knowledge of experimentation design, statistical power, and analysis.
• Strong product instincts focused on customer behavior and business outcomes.
• Clear and confident communication skills for various audiences.
• Comfort with ambiguous and evolving analytical problems.

Nice to have:

• Experience in payments, fintech, or financial services with knowledge of interchange, scheme rules, authorization flows, settlement, or card issuing/acquiring.
• Proficiency in Python or R for statistical analysis and modeling.
• Experience with modern analytics engineering tools (e.g., dbt, CI/CD for data).
• Familiarity with unit economics, margin analysis, or P&L thinking.

Bonus:

• Exposure to payment routing optimization, decline recovery, or acceptance rate analytics.
• Familiarity with experimentation platforms like LaunchDarkly or Optimizely.

• Work closely with Product Managers as an analytical thought partner to frame problems and validate hypotheses.
• Investigate metric changes, customer behavior, and product performance proactively.
• Identify high-value opportunities and risks from data to guide product prioritization.
• Translate complex analyses into clear recommendations.

Data Exploration:

• Analyze transaction-level data to detect spend behavior patterns.
• Estimate opportunities to grow GMV and improve unit economics.
• Quantify revenue and margin impacts of product changes like interchange optimization and billing improvements.
• Explore ambiguous problems by questioning "what if?" and "why not?".

Experimentation:

• Design and analyze A/B tests and quasi-experiments across payment-related areas.
• Establish causal inference frameworks for scenarios without randomization.
• Collaborate with Engineering to ensure quality event instrumentation and experiment infrastructure.

Collaboration:

• Cooperate with Core Data and Engineering on data modeling and pipeline quality.
• Communicate findings effectively to stakeholders including senior leadership.