• Diseñar, entrenar y desplegar modelos de Machine Learning y Deep Learning. • Trabajar con grandes volúmenes de datos estructurados y no estructurados. • Implementar soluciones en AWS utilizando SageMaker. • Optimizar pipelines de datos y procesos de entrenamiento. • Versionar código y modelos con Bitbucket. • Documentar resultados y presentar insights a stakeholders técnicos y de negocio.

Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams - People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more - provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block. The Role This role will be part of the Risk Product Data Science team, focusing on improving risk outcomes while maintaining a strong seller experience across the Payments Risk domain. The Data Scientist will explore new classes of risk controls, size opportunities, assess impact, and evaluate trade-offs. They will also optimize risk decisioning for payments products, drive product improvements, and enhance the end-to-end seller experience. The role partners closely with Product, Engineering, and Risk teams to deliver data-informed insights, guide strategy, and ensure consistent execution of risk initiatives. *Work from anywhere: This role can be performed from any location in the US with the flexibility to work from home You Will Analyze risk actions and quantify impact on losses and seller experience Evaluate effectiveness and trade-offs of risk controls Design, execute, and analyze A/B tests to validate features and optimize risk decisions Apply statistical and modeling techniques to understand customer behavior and inform strategy Collaborate with engineers to log and curate data for analysis Build metrics, dashboards, and visualizations to monitor performance and guide decisions Partner cross-functionally to translate insights into actionable product and risk improvements Communicate findings and recommendations clearly to team leads and stakeholders You Have 8+ years of post-graduate industry experience in product data science; experience in risk is a plus Deep experience in product A/B testing, including experiment design, analysis, interpretation, and guiding decisions under imperfect data Track record of influencing product and risk strategy through data-driven insights and strong cross-functional partnerships Proven ability to build and own metrics, dashboards, and monitoring systems in fast-paced, evolving environments Ability to communicate complex technical concepts clearly to non-technical audiences and influence without authority Strong stakeholder management skills, including navigating trade-offs across Product, Engineering, and Risk Strategic thinker who balances analytical rigor with business pragmatism and long-term impact Proactive mindset with the ability to identify emerging risks and opportunities through data patterns Self-directed operator who can independently drive projects end-to-end after collaborative scoping Bachelor's degree required in Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related quantitative field; Master's or PhD preferred, or equivalent advanced industry experience Technologies We Use and Teach Proficiency in Python (e.g., Pandas, NumPy) and SQL (e.g., Snowflake, BigQuery, MySQL) Experience with analytics and BI tools (e.g., Looker, Mode) Familiarity with Git and version control workflows Strong foundation in probability and statistics, including A/B experiment design and evaluation, statistical inference, and anomaly detection Experience leveraging LLMs and prompt engineering to accelerate development or analyze non-quantitative data We're working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and "fair chance" ordinances. We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we're doing to build a workplace that is fair and square? Check out our I+D page . While there is no specific deadline to apply for this role, U.S. roles are typically open for an average of 55 days before being filled by a successful candidate. Please refer to the date listed at the top of this job page for when this role was first posted. 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.

• Execute and contribute to the design of observational studies using claims data (e.g., case-control matching, difference-in-differences, propensity score methods) • Conduct analyses to measure the cost and outcomes impact of Solera's programs • Investigate product and engagement data to identify patterns, drop-off points, and opportunities to improve outcomes • Build and maintain analysis and modeling pipelines in Python and Spark for feature engineering, cohort construction, and outcomes measurement • Contribute to the team's ML products (e.g., risk models, patient matching) through feature development, evaluation, and iteration • Collaborate cross-functionally with health economics, clinical, product, sales, and engineering teams to interpret results and deliver actionable insights • Document methodologies and findings clearly enough to withstand external scrutiny • Work with cloud data infrastructure (BigQuery, GCS, Dataproc) to query, transform, and analyze large datasets

• Design, build, and implement mathematical models to drive key long-term product goals, like training a model that predicts which of our free users are most likely to subscribe, or recommending the right articles to the right users at the right time. • Lead projects with hands-on analysis and modeling, drawing from multiple analytical methods to choose the right tool and right level of complexity appropriate for the job. • Use quantitative analysis to deeply understand what our users are saying, and to develop recommendations based on that analysis. • Design and develop data science pipelines in production. • Guide complex experiments. • Cultivate an outstanding data science environment.