Related skills
docker python kubernetes hadoop airflow📋 Description
- Build production ML models for regulatory reporting and risk.
- End-to-end ML lifecycle with Spark/Hadoop feature engineering.
- Scale infrastructure for global regulatory reporting.
- Automate root-cause analysis from complex signals.
- Collaborate with product teams to deliver usable solutions.
🎯 Requirements
- 4+ years as ML engineer or data scientist (anomaly/time-series).
- Engineering-first mindset; production ML deployments.
- Python and Big Data: PySpark, Airflow, Hadoop, Kafka.
- SparkStreaming/Flink; Docker and Kubernetes a plus.
- Strong interest in causal inference; explain why.
- Pragmatic, focused on business impact and reliability.
- Proactive in leading projects; clear cross-team communication.
- Able to work in a product team and drive the roadmap.
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