Remote Product Data Scientist: Credit & Risk Innovator

Block; New York (NY); On-site; USD 239,600 - 359,400; Full time

Benefits offered by this job

Remote work flexibility

Medical insurance

Flexible time off

Retirement savings plans

Family planning

Job summary

Block, Inc. is seeking a Product Data Scientist to help build, measure, and improve credit products across Lending offerings for Cash App, Afterpay, and Square loans.

You will partner with product, engineering, and risk to define metrics, run experiments, and translate questions into clear decisions. This agentic data science role uses AI tools and end-to-end analysis to explore datasets, build pipelines, test hypotheses, and drive data-driven product improvements with impact across millions of

Qualifications

• Bachelor’s degree in statistics, data science, economics, computer science, or a similar quantitative field with 12+ years of experience in a relevant role.
• Graduate degree in statistics, data science, economics, computer science, or a similar quantitative field with 6-8+ years of experience in a relevant role.
• Advanced proficiency with SQL and experience building clear, decision-oriented data visualizations.
• Strong product, analytical, and statistical judgment, including the ability to turn ambiguous product, customer, or risk questions into sound analyses, communicate tradeoffs clearly, and support decisions.
• Experience using AI tools to improve the speed, quality, and durability of analytical work.

Responsibilities

• Turn complex product, customer, and risk data into clear insights, decision frameworks, and durable measurement systems for product, risk, and business partners.
• 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.
• Own end-to-end execution across analysis, metrics definition, experimentation, forecasting, visualization, and decision support.
• Define and maintain measurement frameworks for credit products, including customer eligibility, repayment behavior, product usage, loss performance, funnel health, and long-term customer outcomes.
• Partner with risk teams to evaluate model and policy performance, monitor cohorts, identify bias or drift, and connect risk decisions to product and business impact.
• Design and analyze experiments, rollouts, and policy changes that shape customer access, repayment outcomes, and product growth.
• Approach ambiguous product and risk questions from first principles, using statistical judgment to define the right cohorts, metrics, and decision criteria.
• Communicate insights clearly to technical, product, and business stakeholders, including risk partners and senior decision-makers.
• Lead technical direction and standards for the Block Data Science team - making and building consensus on key technical decisions, and creating reusable frameworks, measurement templates, and scalable tooling that remove complexity for others.
• Drive localized cross-team impact by connecting measurement and insights across Lending products (Borrow, Afterpay, Square Loans) and partnering with senior stakeholders in product, engineering, and risk to align Data Science work with broader strategy.
• Mentor and grow the team by developing junior data scientists, fostering a culture of analytical rigor and psychological safety, and contributing to hiring through interviews and calibrations

Skills

SQL

Data visualization

Statistical analysis

AI tools

Education

Bachelor's degree in quantitative field

Graduate degree in quantitative field

Job description

Block, Inc. is seeking a Product Data Scientist to help build, measure, and improve credit products across Lending offerings for Cash App, Afterpay, and Square loans.

You will partner with product, engineering, and risk to define metrics, run experiments, and translate questions into clear decisions. This agentic data science role uses AI tools and end-to-end analysis to explore datasets, build pipelines, test hypotheses, and drive data-driven product improvements with impact across millions of