Data Scientist

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 Role

Square's Sales organization is where we're placing our biggest bets, and the go-to-market (GTM) Data Science team sits at the heart of that growth engine. Reporting to the Sales Data Science lead, you'll own a mature subdomain of our Sales function and contribute across the full channel, alongside peers in Sales Data Science (DS) and the wider GTM Data Science org. The role is a rare combination: you'll be a trusted analytics leader who sales executives turn to for the ground truth on business performance, and a hands-on data scientist who solves big, ambiguous problems end to end. This is a DS team on the front lines of AI, one that uses agents daily to clear away the mundane and free up time for the high-impact, technically challenging work that moves the business forward. You'll grow here: there's no shortage of hard problems, plenty of sharp colleagues to learn from, and the leverage to scale your impact well beyond your own domain.

You Will

• Own performance measurement for your subdomain: monitoring the metrics that matter (wins, new revenue, funnel conversion, rep productivity), reporting against plan, and presenting the story in business reviews with sales leadership
• Lead investigations into performance questions ("why is X down?", "how can we improve Y?"), decomposing KPIs from first principles and designing experiments to test what actually works
• Take on large, ambiguous projects autonomously: scoping the problem, finding the right data, thinking critically about what it can and can't tell you, and landing the results with stakeholders
• Own and evolve your subdomain's data foundation, and partner with Sales DS peers on shared metrics, attribution logic, and cross-cutting analyses
• Translate data into business context: telling the story behind the numbers, pushing back on hypotheses with evidence, and building trust with senior sales, finance, and operations leaders
• Work AI-natively: use agents throughout your workflow and pioneer new ways of applying AI to the hardest problems in the sales domain
• Build self-serve dashboards and datasets that let the sales org answer its own questions
• Raise the bar for the team's analytical rigor, communication, and AI-enabled ways of working

You Have

• Minimum of 8 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or a PhD with 3 years experience; or equivalent experience
• Advanced SQL and strong Python; comfort owning the full stack from ETL to analysis to visualization
• Strong statistical foundations: experiment design, causal inference, funnel and cohort analysis, and forecasting; you can interpret results with rigor and explain the concepts behind them
• A track record of running large projects end to end with minimal direction; you're the person leadership trusts when the numbers matter
• Strong critical thinking: you know when to move fast and when to slow down on a problem with big consequences, and you pressure-test your own conclusions
• Exceptional communication: you can present performance to executives, translate technical nuance for non-technical partners, and write analysis documents that stand on its own
• Enthusiasm for building with AI, and curiosity about how far it can be pushed in data science work
• Curiosity about unfamiliar data and how sales organizations actually work, and the empathy to partner well with the people on the frontline

Nice to have, but not required:

• Prior experience supporting sales, GTM, or revenue organizations
• Familiarity with fintech, payments, or the SMB merchant space

We value strong fundamentals and curiosity over industry background; we'll teach you our domain.

Technologies We Use and Teach

• SQL, Snowflake, Databricks, Python (Pandas, NumPy)
• Looker, Omni, Airflow, dbt-style ETL patterns
• Agentic AI: agent skills and bots, automated reporting workflows, in-house tooling
• Salesforce and the modern GTM data stack
• A/B, matched-market, and causal testing

Pay Transparency:

Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future. To find a location's zone designation, please refer to this resource. If a location of interest is not listed, please speak with a recruiter for additional information.

Zone A: ($198,000 - $297,000)

Zone B: ($188,100 - $282,100)

Zone C: ($178,200 - $267,400)

Zone D: ($168,300- $252,500)