Data Science Visualization Jobs in Windsor, CA (NOW HIRING)

Since we opened our doors in 2009, the world of commerce has evolved immensely, and so has Square. After enabling anyone to take payments and never miss a sale, we saw sellers stymied by disparate, outmoded products and tools that wouldn't work together.
So we expanded into software and started building integrated, omnichannel solutions - to help sellers sell online, manage inventory, offer buy now, pay later functionality, book appointments, engage loyal buyers, and hire and pay staff. Across it all, we've embedded financial services tools at the point of sale, so merchants can access a business loan and manage their cash flow in one place. Afterpay furthers our goal to provide omnichannel tools that unlock meaningful value and growth, enabling sellers to capture the next generation shopper, increase order sizes, and compete at a larger scale.
Today, we are a partner to sellers of all sizes - large, enterprise-scale businesses with complex operations, sellers just starting, as well as merchants who began selling with Square and have grown larger over time. As our sellers grow, so do our solutions. There is a massive opportunity in front of us. We're building a significant, meaningful, and lasting business, and we are helping sellers worldwide do the same.

The Data Science team at Block turns insights from our 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.

• 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

• 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

• SQL, Snowflake, etc.
• Python (Pandas, Numpy)

We're working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and "fair chance" ordinances.
We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we're doing to build a workplace that is fair and square? Check out our I+D page.

While there is no specific deadline to apply for this role, U.S. roles are typically open for an average of 55 days before being filled by a successful candidate. Please refer to the date listed at the top of this job page for when this role was first posted.