Decision Scientist – Dispute Automation

As a Decision Scientist, you will apply data-driven techniques to identify new opportunities, 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).

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”

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.

Zone A: USD $142,000 – USD $170,000
Zone B: USD $134,900 – USD $160,000