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
As a Decision Scientist, you will apply data-driven techniques to identify newopportunities, participate in project definition and prioritization and directly impact business strategy setting outcomes. There is a high expectation of proactively identifying, investigating, and resolving problems with data; designing and establishing key metrics; and the effective usage of various techniques such as statistical modeling and A/B experimentation to drive the best decisions.You will be responsible for the implementation, documentation, and on-going monitoring associated with any customer-facing decisions and risk controls you make.
Decision Scientists are not responsible for the direct development of advanced ML algorithms, but they work closely with ML teams to inform model performance evaluations. Therefore, you possess a general understanding of how ML models work and how to leverage their output for decision making and actioning. You may also be expected to create offline ML models for the purpose of facilitating decision making, such as forecasting and segmentation.
This role is part of our Cash App's ML team and will be deeply embedded within one of those teams - below is more about the team that this Disputes Automation Decision Scientist role is embedded in and how this role fits into the team:
Risk (Cash Card ML) - This team owns analytical and ML solutions for the full lifecycle of the Cash Card product with a focus on mitigating risk/fraud. Team works cross-functionally with Product, Operations, Legal, and others to balance financial loss to the business, regulatory risk, and the holistic customer experience.
Decision Scientists on the team focus on the defining and implementation of automation policies through our in house rules engine that affect customer initiated Cash Card disputes. Decision Scientists in this role will work closely with specialized risk operation teams and Product to implement automation strategies based on legally defensible documented policies and outcomes. Additionally, by building on the success of the team for Cash Card, Decision Scientists in this Disputes Automation role have expanded to support disputes in other transaction types (P2P, $pay, etc).
Qualifications
Technologies we use (and teach):

• SQL, Snowflake, GCP/AWS, Tableau, Looker
• Python (NumPy, Pandas, sklearn, etc.)

You have:

• Advanced degree in Mathematics, Statistics, Computer Science, Economics or other quantitative field
• 3+ years of experience in a data-driven decision making role in a domain that leverages applied analytics to affect customer experiences regularly such as risk, lending, or customer support
• High proficiency with SQL and working within complicated data domains
• Proficient at defining, implementing, communicating and monitoring performance metrics with a variety of stakeholders including Product, ML, Operations, Legal, external stakeholders, etc.
• Solid experience with data analysis and scripting in Python
• Some familiarity with ML techniques, applications and best practices in solving real world problems
• Proven track record of tackling ambiguous business challenges with minimal guidance and applying analytical/statistical methods to tackle real-world problems using big data"

Qualifications
Technologies we use (and teach):

• SQL, Snowflake, GCP/AWS, Tableau, Looker
• Python (NumPy, Pandas, sklearn, etc.)

You have:

• Advanced degree in Mathematics, Statistics, Computer Science, Economics or other quantitative field
• 3+ years of experience in a data-driven decision making role in a domain that leverages applied analytics to affect customer experiences regularly such as risk, lending, or customer support
• High proficiency with SQL and working within complicated data domains
• Proficient at defining, implementing, communicating and monitoring performance metrics with a variety of stakeholders including Product, ML, Operations, Legal, external stakeholders, etc.
• Solid experience with data analysis and scripting in Python
• Some familiarity with ML techniques, applications and best practices in solving real world problems
• Proven track record of tackling ambiguous business challenges with minimal guidance and applying analytical/statistical methods to tackle real-world problems using big data"