Staff Data Scientist AI and Pricing

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.
• 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.

Nice-to-Haves

• Prior exposure to a pricing-adjacent domain a strong plus — risk-based pricing (payments, lending, insurance), pricing science, or deal pricing analytics.
• Experience building, testing, or evaluating LLM-powered systems in production a strong plus.

Technologies

• SQL, Snowflake
• Python (Pandas, Numpy)