SEE ALL JOBS; Data Scientist Apply Remote Bay Area, CA, US Req ID: R0006749 Posted Date: 07/20/26 Posted 2 days ago Apply Job DetailsApply 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...
Want jobs like this matched to you?
Swoopd scores fresh postings against your résumé so you only see the matches that matter.