Machine Learning Engineer, Commercial Fraud Risk (Modeling)

Company Description

Block initiated an idea in 2013 which soon developed into Cash App, created to simplify peer-to-peer payments. What sprung from a simple, singular-purpose product has now swelled into an expansive financial ecosystem, offering distinctive financial solutions including Afterpay/Clearpay. Our intent is to offer a seamless way to send, invest, extract, save and spend money to our 47 million customers on a monthly basis. Our vision is to transform the world's conventional engagement with money into a simpler, instant and universally reachable experience.

Currently, Cash App is backed by a global work force, both positioned in office spaces and working remotely, which focuses on pioneering, teamwork and creating an impact. Our foundation is built on our distributed team model and we have specifically designed several roles to be performed from remote locations where Cash App operates. We concentrate on crafting an environment for our employees where they can unleash their creativity, ensure productivity and are content, regardless of their location.

To find out more about our locations, perks and other information, visit

Job Description

The Fraud Risk Team is dedicated to developing commerce fraud risk strategy and supporting merchant risk teams by providing them with models and risk indicators to oversee critical risk metrics. We work hand-in-hand with numerous engineering teams to pave the path for serving next-gen AI solutions. 

In the capacity of a Machine Learning Modeler on the Fraud Risk Team, your duty will be the development and implementation of machine learning models to detect and prevent fraudulent actions. A deep understanding of machine learning algorithms, data analysis and techniques to detect fraud is a requirement for this role.

Your responsibilities:

• Developing, implementing, and maintaining machine learning models for fraud detection.
• Analyzing large datasets to uncover patterns and trends linked to fraudulent activities.
• Working closely with cross-functional teams to understand business requirements and generate solutions to reduce fraud risks.
• Constantly monitoring and evaluating the performance of machine learning models, and making alterations as required.
• Keeping up-to-speed with the latest evolutions in machine learning and fraud detection, and incorporating appropriate new techniques and technologies into our processes.
• Preparing and presenting reports on model performance and fraud trends to stakeholders.

Qualifications

The ideal candidate will have:

• A Bachelor's degree in fields like Computer Science, Statistics, Mathematics, or a related domain. A Master's degree is preferred.
• At least 5 years of experience in machine learning, data analysis, or a relevant field.
• Demonstrable experience in detecting fraud and risk management.
• Deep understanding of machine learning algorithms and data analysis techniques.
• Experience in programming languages such as Python or Java.
• Outstanding problem-solving skills and meticulous attention to detail.
• Excellent communication skills, with the capacity to express complex ideas in a simple manner to non-technical stakeholders.

Technologies we use and teach:

• Python (including libraries like NumPy, Pandas, sklearn, TensorFlow, , keras, etc.)
• Snowflake, DataBricks, GCP, AWS
• Conventional classification / regression models. Advanced Learning including Sequential modeling, Graph modeling and Transformer based models

Additional Information

At Block, our pay system depends on the market. Pay may vary due to your location. We have categorized U.S. locations into four zones based on the labor cost index of that specific area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges might be altered in the future.

Zone A: USD $167,300 - USD $204,500
Zone B: USD $158,900 - USD $194,300
Zone C: USD $150,600 - USD $184,000
Zone D: USD $142,200 - USD $173,800