Key Responsibilities:
Own the design and implementation of robust ML models for detecting
bot traffic and wasteful traffic, ensuring seamless deployment,
versioning, and integration with production systems.
Drive experimentation and prototyping of new algorithms and data
products, validating their potential impact.
Conduct research to surface and detect emerging ad fraud patterns
and evolving threats in large-scale traffic data.
Monitor and optimize model performance post-deployment,
proactively addressing issues such as model drift, latency, and false
positives.
Influence product direction by identifying opportunities to turn insights
and patterns into features or offerings for clients; collaborating cross-
functionally with Engineering, Product, and Commercial teams to
translate business needs into data-driven solutions.
Ensure the reliability, security, and compliance of ML systems with
privacy regulations, infrastructure constraints, and real-world trade-
offs.
Contribute to internal technical standards, mentor peers, and help grow
a culture of high-quality, impact-focused data science work.
Stay at the forefront of ML and ad-fraud detection techniques, bringing
relevant innovations into our product and research roadmap.
Requirements:
Strong experience designing, developing and maintaining ML models.
Deep proficiency in Python, SQL and cloud infrastructure - working with
large datasets, and real-time prediction applications.
Proven experience with AWS, including SageMaker, Lambda, and data
services (e.g., S3, Redshift, etc.).
Familiarity with MLOps best practices.
Comfortable running experiments, building prototypes, and contributing
to product and feature design from a data science perspective.
A strong grasp of system-level thinking - including privacy, compliance,
model explainability, and real-world impact.
Practical and pragmatic, able to balance technical excellence with
shipping value quickly.
Experience in adtech, media or fraud detection is a plus!
