The business problem is to lead advanced analytics and ML initiatives for airlines/aero programs, spanning flight telemetry, aircraft health monitoring, operational efficiency, delay attribution, fuel optimization, and safety insights for an aviation company in Chicago, Illinois.
Requirements
- 7+ years in Data Science with 3+ years in aviation or large-scale event/time-series domains.
- Strong in Python, SQL, Spark; hands-on with time-series, anomaly detection, Bayesian methods.
- AWS: SageMaker, S3, Glue, EMR, Lambda, Step Functions, CloudWatch.
- Streaming/event data: Kafka/MSK or Kinesis, Spark Structured Streaming/Flink.
- MLOps: MLflow/Kubeflow/SageMaker, CI/CD, IaC.
- Model monitoring & explainability (drift, SHAP/LIME)
- FOQA, QAR/DFDR, ACMS, AID, ATA chapters, MSG-3/CBM, AMOS/RAMCO/TRAX.
Responsibilities
- Translate business problems into ML solutions.
- Build models for time-series forecasting, anomaly detection, survival analysis, clustering, and optimization.
- Engineer features from flight logs, ACARS, ADS-B, maintenance logs, and weather data.
- Productionize models on AWS with CI/CD, model registry, feature store, and monitoring.
- Collaborate with Data Engineering to ensure data quality, lineage, and governance.
- Communicate insights and model decisions to non-technical stakeholders.
- Own end-to-end model lifecycle from problem framing to production in AWS (multi-cloud is a plus)
Other
- 7+ years of experience in Data Science
- Full-time job
- Onsite only in Chicago, Illinois
- Collaborate with Data Engineering
- Communicate insights and model decisions to non-technical stakeholders
