• Lead, and be accountable for the success of complex AI and data programs, serving as primary point of contact for client sponsors and partners. • Partner closely with technical delivery leads and execution teams to drive success and measurable client outcomes. • Translate client strategy into clear roadmaps and integrated delivery plans, balancing pace, quality, and risk across multi-geo teams. • Own and actively manage scope across all parties involved. Own or co-own engagement financials (budget, burn, margins, forecasting), early risk/variance management using delivery metrics (e.g., burn, velocity, adoption, value realization), associated communication, and change control as necessary. • Build strong, trust-based relationships with senior client and partner business and technology stakeholders. • Navigate ambiguity, conflicting priorities, and tough trade-offs with calm, data-informed recommendations. • Coordinate effectively with ecosystem partners (e.g., Snowflake, ISVs) to align roadmaps, funding, and delivery. • Shape a healthy, inclusive delivery culture by setting sustainable operating rhythms and coaching PMs/Delivery Leads/consultants. • Contribute to the ongoing iteration and refinement of existing phData processes and methodologies.

• Own Dave's portfolio of data and risk partners across fraud, identity, open banking, bank connectivity, and credit bureaus • Turn Dave's product, risk, and business objectives into a clear partner roadmap, sequencing renewals, evaluations, and new capabilities • Evaluate new partners against commercial fit, data quality, risk impact, compliance, cost, and resiliency • Build scorecards that hold partners accountable: service levels, cost, approval and conversion impact, fraud and risk outcomes, data reliability, and contractual obligations • Find openings to sharpen Dave's underwriting and fraud controls through better partner capabilities • Run each partnership end to end: sourcing, diligence, business case, financial modeling, negotiation, contracting, implementation, and post-launch performance • Lead partner governance, renewal strategy, issue escalation, and risk mitigation • Build relationships with senior leaders at data providers, infrastructure companies, risk platforms, and banks

• Develop machine learning and AI systems that improve decisions across pricing, underwriting, portfolio management, operations, and claims. • Work across structured data, text, documents, and external data sources, applying statistical modeling, modern machine learning, AI and agentic workflows to solve challenging real-world problems. • Identify the right approach, build production-ready solutions, and measure the business impact of your work. • Tackle a broad range of machine learning and AI problems such as predictive modeling, classification, ranking, matching, recommendation, anomaly detection, information extraction, entity resolution, building high-quality datasets, and automating analytical and decision-making workflows.

Role Description 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. Qualifications - 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. Requirements - 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. Benefits - Remote work. - Medical insurance. - Flexible time off. - Retirement savings plans. - Modern family planning.