We are seeking a Senior Data Scientist with deep expertise in aviation analytics and large-scale event/time-series data. This role involves developing and deploying advanced ML models to support critical aviation use cases such as aircraft health monitoring, operational efficiency, delay attribution, and safety insights.
Required Skills
- 7+ years of experience in Data Science, including 3+ years in aviation or large-scale time-series domains
- Proficiency in Python, SQL, Apache Spark
- Strong background in time-series analysis, anomaly detection, and Bayesian modeling
- Experience with AWS services: SageMaker, S3, Glue, EMR, Lambda, Step Functions, CloudWatch
- Hands-on with streaming/event data: Kafka/MSK or Kinesis, Spark Structured Streaming, or Flink
- Experience in MLOps: MLflow, Kubeflow, or SageMaker Pipelines; CI/CD and Infrastructure-as-Code (IaC)
- Familiarity with model monitoring and explainability (e.g., drift detection, SHAP, LIME)
Key Responsibilities
- Translate complex aviation problems into ML/AI solutions
- Develop models for forecasting, anomaly detection, clustering, survival analysis, and optimization
- Engineer features from flight logs, ACARS, ADS-B, maintenance data, and weather feeds
- Deploy models in production on AWS using CI/CD pipelines and MLOps practices
- Ensure data quality, lineage, and governance in collaboration with Data Engineering
- Communicate findings clearly to both technical and non-technical stakeholders
Preferred Qualifications
- Experience with aviation data systems: FOQA, QAR/DFDR, ACMS, AID, ATA chapters, MSG-3/CBM
- Familiarity with tools like AMOS, RAMCO, TRAX
- Exposure to ADS-B, ACARS, IATA SSIM, OOOI timestamps, and delay/fuel analytics
- Knowledge of safety programs: ASAP, SMS, LOSA; regulatory exposure (ICAO Annex 19, EASA, FAA)