Staff Product Data Scientist, Lending

Amit érdemes tudni

• Irodai · Korlátozott
• Egyesült Államokbeli irodai pozíció.
• Szint: Lead
• Bitcoin-fókuszú szerep
• Fizetés: nem közölt

Helyszín és jogosultság

Egyesült Államokbeli irodai pozíció.

New York, NY, United States of America

Megbízhatóság: 90%. A végső döntés a munkáltatóé – mindig olvasd el az eredeti hirdetést.

Pozíció

<p>It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world’s relationship with money to make it more relatable, instantly available, and universally accessible.<br><br>Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We’ve been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy.</p> <h4><strong>The Role</strong></h4> <p>The Data Science team at Block turns unique customer and product data into decisions that expand access to financial services. Our Lending team powers decisioning behind Cash App Borrow, Afterpay, Square Loans, and the next generation of first-party credit products.</p> <p>We’re looking for a Product Data Scientist to help build, measure, and improve credit products that serve customers traditional credit systems often miss. You’ll partner closely with product, engineering, and risk teams to define metrics, evaluate experiments, understand customer behavior, and turn ambiguous product questions into clear decisions.</p> <p>This is an agentic data science role. You’ll use AI tools and agent workflows to move faster and think more rigorously across the full data science loop: exploring messy datasets, building pipelines, stress-testing hypotheses, evaluating product changes, and turning analysis into decisions.</p> <h4><strong>You Will</strong></h4> <ul> <li>Turn complex product, customer, and risk data into clear insights, decision frameworks, and durable measurement systems for product, risk, and business partners</li> <li>Use AI tools and agent workflows to improve both the speed and quality of analytical work, from accelerating exploration and automating repetitive tasks to generating hypotheses and stress-testing conclusions</li> <li>Own end-to-end execution across analysis, metrics definition, experimentation, forecasting, visualization, and decision support</li> <li>Define and maintain measurement frameworks f

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