We’re hiring a Senior Data Scientist to partner with our newly formed Payments team in Poland, focused on optimizing how Dropbox collects, processes, and retains revenue. This team is responsible for modernizing our payments stack—improving authorization rates, reducing involuntary churn, evaluating third-party payment tools, and driving strategic platform investments.
You’ll work closely with product, engineering, and finance stakeholders to uncover insights across the payments lifecycle, inform roadmap prioritization, and build the measurement systems that drive data-informed decisions at scale. Ideal candidates bring strong product intuition, technical rigor, and experience working in SaaS or monetization-focused environments.
- Develop a deep understanding of the end-to-end payments lifecycle—from checkout to re-bill to chargebacks—and define metrics that reflect health and opportunity.
- Conduct deep dives and build models to diagnose payment failures, forecast impact of retry or routing strategies, and uncover drivers of churn or revenue leakage.
- Partner with product and finance to evaluate third-party payment tools (e.g., fraud protection, processors, vaults) through quantitative impact analysis.
- Build and maintain executive dashboards and self-serve tools that monitor authorization rates, churn, processor performance, and cost optimization opportunities.
- Design and analyze experiments (e.g., Apple Pay, network tokenization, retry tuning) to evaluate interventions that improve authorization and recovery rates.
- Translate complex findings into clear, actionable recommendations—helping shape both strategic direction and near-term prioritization.
- Collaborate across functions, including Product, Engineering, Design, and other Data Scientists, to embed insights into decision-making and long-term planning.
- Minimum of 8 years experience in product, business, or marketing analytics—ideally in a SaaS or payments-related domain.
- Bachelor’s or higher in a quantitative field (e.g., Statistics, Applied Math, Economics, Computer Science, Engineering).
- Expertise in SQL and working with large-scale data systems; familiarity with Hadoop, Spark, or similar platforms is a plus.
- Strong foundation in experimental design, A/B testing, regression modeling, and statistical inference.
- Experience with payments, monetization funnels, or subscription lifecycle analysis is strongly preferred.
- Effective communicator with a demonstrated ability to influence product decisions through data storytelling.
- Proficiency in R, Python, or similar scripting/statistical languages is a plus.
- Master's or Ph.D. Degree in a quantitative field
- Experience with predictive modeling, machine learning, and experimentation/causal inference methods.
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