Job Description

What You Will Be Doing

Develop, train, and deploy ML models to detect automation and fraud across large-scale, high-velocity traffic.

Explore massive datasets to identify anomalies, uncover new adversarial behaviours, and anticipate attack vectors.

Build and maintain scalable pipelines and frameworks that can handle real-time detection in adversarial environments.

Collaborate closely with engineers, researchers, and product managers to operationalise models and integrate them into Kasada’s platform.

Design experiments to rigorously evaluate detection effectiveness and resilience against attacker adaptation.

Stay on the cutting edge of adversarial ML and cybersecurity, translating research into practical defence capabilities.

Communicate insights and outcomes clearly, ensuring technical and business stakeholders can act on findings.

What You’ll Bring

Strong expertise in machine learning, statistics, and anomaly detection methods.

Proficiency with Python and ML libraries (pandas, scikit-learn, PyTorch/TensorFlow).

Hands-on experience with big data platforms (such as Flink and Spark).

Proven ability to work with AWS services for large-scale data processing and ML deployment.

Comfort with messy, high-volume datasets and designing scalable, production-ready solutions.

Excellent problem-solving and analytical skills, with sharp attention to detail.

Background in cybersecurity, fraud detection, or adversarial ML (preferred).

Familiarity with real-time frameworks (Kafka, Kinesis) and advanced detection methods (clustering, graph analysis).

Strong communication and collaboration skills — able to influence, teach, and align diverse stakeholders.

Advanced degree (MSc/PhD) in Computer Science, Data Science, or related field is a plus.

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