Minimum of 2 years of experience in a leadership role in a ML or data science team in an agile, fast-paced environment
At least 5 years of professional experience in software engineering, machine learning, or a related technical discipline
Demonstrated ability to lead technical teams that ship production ML systems — from data pipelines and feature engineering through model training, evaluation, and deployment
Proven track record of building and developing high-performing, multidisciplinary teams including engineers, data scientists, and/or analysts
Strong communication skills with the ability to translate complex model behavior, data quality issues, and technical tradeoffs to both technical teammates and non-technical stakeholders
Demonstrated success driving outcomes in fast-moving, scaling environments where priorities evolve and ambiguity is the norm
Experience managing teams that work with large-scale behavioral or event data in a production setting
Familiarity with ML infrastructure and MLOps tooling — experiment tracking (e.g., MLflow), feature stores, model registries, and CI/CD pipelines for ML
Background in fraud detection, identity, or trust & safety domains is a plus but not required
Hands-on experience with data stack technologies such as dbt or similar analytics engineering tooling
Comfort working closely with platform and API engineering teams to understand downstream requirements and latency constraints