Machine Learning Modeler, Commercial Fraud Risk

Company Description

Everything began with an idea at Block in 2013. Initially created to alleviate the difficulty of peer-to-peer payments, Cash App has evolved from a straightforward product with a singular function to a versatile ecosystem, establishing distinctive financial products, including Afterpay/Clearpay, to offer a superior method to send, spend, invest, borrow, and save to our 47 million monthly active users. We strive to revolutionize the world's association with money, making it more comprehensible, instantly accessible, and universally attainable.

Now, Cash App employs thousands of people globally across office and remote sites, fostering a culture that encourages innovation, cooperation, and influence. We've been a distributed team since our inception, and many of our roles can be performed remotely from the countries where Cash App operates. Irrespective of the location, we customize our experience to ensure our employees are imaginative, productive, and satisfied.

Explore our locations, benefits, and more at

Job Description

The Fraud Risk Team facilitates the commerce fraud risk strategy and merchant risk departments with models and risk indicators to manage the risk key metrics. We work together with multiple engineering teams to create the capacity for implementing next-generation AI solutions.  

As a Machine Learning Modeler on the Fraud Risk Team, your responsibility will be to develop and execute machine learning models to identify and prevent fraudulent activities. This role demands a robust comprehension of machine learning algorithms, data analysis, and fraud detection mechanisms.

Your responsibilities will include:

• Development, implementation, and maintenance of machine learning models for fraud detection.
• Analysing vast datasets to identify patterns and trends associated with fraudulent activities.
• Team collaborations to understand business requirements and develop solutions to reduce fraud risks.
• Ongoing monitoring and evaluation of the performance of machine learning models, making adjustments when required.
• Remaining updated with the latest advancements in machine learning and fraud detection, and integrating new techniques and technologies into our processes accordingly.
• Preparation and presentation of reports on model performance and fraud trends to stakeholders.

Qualifications

You possess:

• A Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field. A Master's degree is preferred.
• Five years of experience or more in machine learning, data analysis, or a related field.
• Demonstrated experience in fraud detection and risk management.
• Solid knowledge of machine learning algorithms and data analysis techniques.
• Proficiency in programming languages such as Python or Java.
• Exceptional problem-solving skills and attention to detail.
• Strong communication skills, with the capacity to explain intricate concepts to non-technical stakeholders.

Technologies we utilize and instruct:

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

Additional Information

Block employs a market-based approach for compensation, and pay may vary depending on your location. U.S. locations are divided into one of four zones based on the cost of labor index for that geographic area. The successful applicant's starting pay will be determined based on job-related abilities, experience, qualifications, work location, and market conditions. These ranges may be modified 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