Remote Payments Data Scientist – AI-Driven Analytics

Block; Sacramento (CA); Hybrid; USD 112,000 - 168,000; Full time

Benefits offered by this job

Remote work

Medical insurance

Flexible time off

Retirement savings plans

Family planning

Job summary

Block is seeking a data science professional early in their career to analyze payments data across Square and Cash App. You’ll collaborate with Product, Operations, Finance, and Engineering to optimize the payments platform from success rate to cost efficiency and risk mitigation.

You will build ETL pipelines, dashboards, and ML models, using SQL and Python, while translating business needs into practical data solutions and improving data workflows across the org.

Qualifications

• Bachelor's degree in a quantitative field with strong academic record.
• Experience coding during an internship and with large datasets.
• Ability to use AI tools to speed up development and automate processes.
• Strong analytical and business acumen; ability to communicate with non-technical partners.

Responsibilities

• Build ETL pipelines, dashboards, and ML models for payments data.
• Partner with Product, Ops, Finance, and Engineering to drive data-driven decisions.
• Design experiments and translate business needs into data solutions.
• Maintain data systems and cloud infrastructure for scalable analytics.
• Measure cost and performance of the payments stack and optimize it.

Skills

Bachelor's degree

Internship coding

SQL

Python

AI tools

Data analysis

Communication

Cloud infra

Payments knowledge

Education

Bachelor's degree in a quantitative field

Tools

SQL

Python

Job description

Block is seeking a data science professional early in their career to analyze payments data across Square and Cash App. You’ll collaborate with Product, Operations, Finance, and Engineering to optimize the payments platform from success rate to cost efficiency and risk mitigation.

You will build ETL pipelines, dashboards, and ML models, using SQL and Python, while translating business needs into practical data solutions and improving data workflows across the org.