Senior Data Scientist, P2P

Company Description

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 47 million monthly active customers. We want to redefine the world’s relationship with money to make it more relatable, instantly available, and universally accessible.

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.

Check out our locations, benefits, and more at cash.app/careers.

Job Description

The Data Science team at Cash App derives valuable insights from our extremely unique datasets and turns those insights into actions that improve the experience for our customers every day. In this role, you’ll be embedded in our Operating Systems organization and work closely with product management as well as other cross-functional partners to own our peer-to-peer (P2P) transfers product. 

You Will

• Analyze large datasets using SQL and scripting languages to surface actionable insights and opportunities to the product team and other key stakeholders
• Approach problems from first principles, using a variety of statistical and mathematical modeling techniques to research and understand customer behavior
• Design and analyze A/B experiments to evaluate the impact of changes we make to the product
• Work with engineers to log new, useful data sources as we build new product features
• Build, forecast, and report on metrics that drive strategy and facilitate decision making for key business initiatives
• Write code to effectively process, cleanse, and combine data sources in unique and useful ways, often resulting in curated ETL datasets that are easily used by the broader team
• Build and share data visualizations and self-serve dashboards for your partners
• Effectively communicate your work with team leads and cross-functional stakeholders on a regular basis

Qualifications

You have:

• Previous exposure to or interest in areas like anomaly detection or regulatory data science 
• A bachelor degree in statistics, data science, or similar STEM field with 7+ years of experience in a relevant role OR
• A graduate degree in statistics, data science, or similar STEM field with 5+ years of experience in a relevant role
• Advanced proficiency with SQL and data visualization tools (e.g. Tableau, Looker, etc)
• Experience with scripting and data analysis programming languages, such as Python or R
• Gone deep with cohort and funnel analyses, a deep understanding statistical concepts such as selection bias, probability distributions, and conditional probabilities

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 also consider qualified applicants with criminal histories for employment on our team, and always assess candidates on an individualized basis.

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.

Block will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.