Description
The Data Science team at Block turns insights from unique datasets into actions that improve the customer experience every day.
In this role, we're looking for a Data Scientist to own the modeling and experimentation at the core of how Square prices globally. You'll build the elasticity and willingness-to-pay models, design and run the pricing experiments, and stand up the analytical infrastructure that makes pricing measurable and controllable , shaping pricing strategy and deal-desk automation through the models and experiments you build.
Responsibilities:
- Model price elasticity and willingness-to-pay across segments, geographies, and payment methods, and quantify the trade-off between margin, conversion, and merchant retention
- Design, run, and read out pricing experiments (A/B, difference-in-differences, and bandit-based dynamic tests) and translate results into recommendations that shape strategy
- Decompose merchant economics across interchange, scheme, and risk-cost layers to identify where pricing can flex and where it can't
- Build the pricing intelligence that powers Square's agentic deal tooling (DealBot) , rate recommendations, ROI and pre-approval logic, guardrail configurations, and mispricing detection , so quotes are fast, accurate, and within guardrails at scale
- Evaluate and monitor the AI systems you ship , pre-deployment testing for accuracy, boundary and edge cases, and bias in rate recommendations, and in-production monitoring for accuracy, drift, and mispricing , so agentic pricing tools stay reliable as the business changes
- Own end-to-end execution across the stack , analysis, pipeline, ETL, experimentation, and visualization
- Approach problems from first principles, using a variety of statistical and modeling techniques to understand customer behavior and price response
- Build and maintain the pricing analytics the team relies on , price realization, margin leakage, discount-waterfall, and win/loss analyses , as self-serve dashboards and curated datasets
- Measure the impact of AI-driven pricing automation with causal methods (interrupted time series, difference-in-differences) on deal velocity, quote acceptance, and margin
- Write code to process, cleanse, and combine data sources into curated ETL datasets easily used by the broader team
- Partner closely with cross-functional stakeholders across Finance, Risk, Product, and go-to-market teams, translating complex technical and AI concepts clearly for non-technical audiences
Requirements:
- A bachelor degree in statistics, data science, economics, or similar STEM field with 7+ years of experience in a relevant role OR a graduate degree in statistics, data science, economics, or similar STEM field with 5+ years of experience in a relevant role
- Fluency in causal inference and experimentation, with hands-on experience modeling price elasticity or willingness-to-pay
- Prior exposure to a pricing-adjacent domain a strong plus , risk-based pricing (payments, lending, insurance), pricing science, or deal pricing analytics
- Advanced proficiency with SQL and data visualization tools (e.g. Tableau, Looker, etc)
- Experience with scripting and data analysis programming languages, such as Python or R, including using them to evaluate AI system behavior
- Gone deep with cohort and funnel analyses, with a solid understanding of statistical concepts such as selection bias, probability distributions, and conditional probabilities
- Comfort leveraging AI tools to accelerate modeling and analysis, and a working understanding of generative AI architectures , LLMs, RAG systems, and agentic AI; experience building, testing, or evaluating LLM-powered systems in production a strong plus