Description:

Bridge the gap between data science and large-scale distributed systems.
Build end-to-end pipelines for model training and predictions at production scale with low latency.
Work on an automated machine learning ecosystem that recalibrates models based on streaming data.

Requirements:

4+ years of experience in building and deploying large-scale machine learning models in high-traffic environments.
Proficiency in Java or Scala for backend, and Python for data analysis and model prototyping.
Experience with Databricks, Apache Spark, Apache Flink, or Hadoop, and NoSQL data stores like Bigtable.
Strong understanding of statistical modeling, probability, and machine learning algorithms.
Ability to reason through data consistency and performance constraints in a distributed cloud environment (GCP).

Opportunity to work in a diverse, equitable, and inclusive workplace.
Contribute to building a safer Internet through innovative fraud detection solutions.

Skills