Job Description

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; designing and establishing key metrics; and the effective usage of various techniques such as statistical modeling, A/B experimentation and machine learning to drive the best decisions. Decision Scientists are not primarily responsible for the development of advanced ML algorithms, but they need to inform model performance evaluation. Therefore, they possess a general understanding of how ML models work and how to leverage their output for decision making and actioning. They are also expected to create offline ML models in the purpose of enlightening 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 our product teams - here are the workstreams we’re currently hiring for:

Risk (Cash Card) -  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, Platform, Legal, and others to balance financial loss to the business, regulatory risk, and the holistic customer experience.

Ads & Incentives - This team is responsible for building data-driven solutions, strategies, in-depth analytics and dashboards in consumer experience in partnership with business stakeholders. This team drives monetization throughout the Cash App ecosystem and drives consumer engagement in delivering growth. 

Qualifications

Technologies we use (and teach):

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

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 ML regularly such as recommendation, ads and NLP
• Solid experience with data analysis and scripting in Python and proficient in SQL
• Familiarity with ML techniques, applications and best practices in solving real world problems
• Proficient at defining, utilizing and communicating performance metrics with Product, Design and Engineering, etc.
• Proven track record of tackling ambiguous business challenges with minimal guidance and applying analytical/statistical methods to tackle real-world problems using big data