Partner with PM, Tech Lead and engineering stakeholders to align deliverables to roadmap milestones.
Own end-to-end ML lifecycle: data, features, training, deployment, monitoring.
Run rigorous experiments: offline metrics, A/B tests, threshold setting, error analysis.
Communicate across time zones; maintain high-quality technical documentation.
Participate in on-call rotation for detection efficacy and realtime scoring.

🎯 Requirements

BS in CS, ML, AI, IS or related quantitative field.
1+ years building operating applied ML in production.
Proven end-to-end ML systems: data wrangling, features, model selection, training, deployment.
Implement algorithms, develop features, combine signals, numerical computing.
Interrogate production data, identify shifts, launch experiments.
Experience with offline/online pipelines, metrics, A/B tests, drift monitoring.

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