he Fraud Risk Team supports the commerce fraud risk strategy and merchant risk teams with models and risk indicators to manage the risk key metrics. We collaborate with multiple engineering teams to create the capacity of serving next generation AI solutions.

As a Machine Learning Modeler on the Fraud Risk Team, you will be responsible for developing and implementing machine learning models to detect and prevent fraudulent activities. This role requires a strong understanding of machine learning algorithms, data analysis, and fraud detection techniques.

You will:

  • Develop, implement, and maintain machine learning models for fraud detection.
  • Analyze large datasets to identify patterns and trends related to fraudulent activities.
  • Collaborate with cross-functional teams to understand business needs and develop solutions to mitigate fraud risks.
  • Continuously monitor and evaluate the performance of machine learning models, making adjustments as necessary.
  • Stay up-to-date with the latest developments in machine learning and fraud detection, and incorporate new techniques and technologies into our processes as appropriate.
  • Prepare and present reports on model performance and fraud trends to stakeholders.


You have:

  • Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field. A Master’s degree is preferred.
  • 5+ years of experience in machine learning, data analysis, or a related field.
  • Proven experience in fraud detection and risk management.
  • Strong knowledge of machine learning algorithms and data analysis techniques.
  • Proficiency in programming languages such as Python or Java.
  • Excellent problem-solving skills and attention to detail.
  • Strong communication skills, with the ability to explain complex concepts to non-technical stakeholders.

Technologies we use and teach:

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

Additional Information

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 $163,600 – USD $245,400
Zone B: USD $155,400 – USD $233,200
Zone C: USD $147,300 – USD $220,900
Zone D: USD $139,000 – USD $208,600

Job Overview
Job alerts

Subscribe to our weekly job alerts below and never miss the latest jobs

Sign in

Sign Up

Forgotten Password

Job Quick Search