Description:

The team is responsible for defining the vision, conducting research, and developing advanced machine-learning models to tackle fraud at scale.
The focus is on delivering clear and accurate risk assessments throughout the entire user journey, covering login attempts, account creation, and payment transactions.
The goal is to eliminate fraud losses and minimize friction for legitimate users, empowering businesses to create seamless, trustworthy experiences.
The company believes that machine learning is key to preventing Account Creation Fraud, Account Takeover (ATO), and Payment Fraud.
Solutions are designed to intelligently detect and mitigate risks, ensuring a secure and resilient online ecosystem.
The role involves researching and applying the latest machine learning algorithms to power the core business product.
Responsibilities include building offline experimentation systems, evolving ML models and architecture, and designing and prototyping a wide range of technologies.
The position requires scaling machine learning pipelines to produce thousands of models from terabytes of data and building systems that explain model predictions.
Data science techniques will be used to analyze fraudulent behavior patterns and collaborate with other teams to enhance machine learning applications within Sift.
The role also involves generating and executing ideas to provide customers with actionable insights to identify and prevent fraudulent behaviors and transactions.

Requirements:

A practical understanding of machine learning and data science concepts, with a track record of solving problems using these methods is required.
Candidates must have 4+ years of experience working with production ML systems.
A minimum of 3 years of experience working with large datasets using Spark, MapReduce, or similar technologies is necessary.
At least 5 years of experience building backend systems using Java, Scala, Python, or other programming languages is required.
Experience in training machine learning models end-to-end is essential.
Strong communication and collaboration skills are needed, along with a belief that team output is more important than individual output.
A degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field is required.

The position offers the opportunity to work at the forefront of AI/ML-driven fraud prevention with cutting-edge technologies.
Employees will have the chance to innovate and develop industry-leading solutions that make a tangible impact in securing the digital world.
The company fosters a culture that values passion for machine learning, cybersecurity, and creating safe online experiences.