Pentangle Tech Services | P5 Group
Job Description:
Check below to see if you have what is needed for this opportunity, and if so, make an application asap. Senior Data Scientist – Aviation Analytics (7+ years) Mandatory Skills: • - 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). Key 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. 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. Own end-to-end model lifecycle from problem framing to production in AWS (multi-cloud is a plus). Preferred Qualifications: • - FOQA, QAR/DFDR, ACMS, AID, ATA chapters, MSG-3/CBM, AMOS/RAMCO/TRAX. • - ADS-B, ACARS, IATA SSIM, OOOI timestamps, delay codes, fuel/weight & balance analytics. • - Safety programs (ASAP/SMS/LOSA), ICAO Annex 19 context; EASA/FAA exposure
Check below to see if you have what is needed for this opportunity, and if so, make an application asap. Senior Data Scientist – Aviation Analytics (7+ years) Mandatory Skills: • - 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). Key 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. 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. Own end-to-end model lifecycle from problem framing to production in AWS (multi-cloud is a plus). Preferred Qualifications: • - FOQA, QAR/DFDR, ACMS, AID, ATA chapters, MSG-3/CBM, AMOS/RAMCO/TRAX. • - ADS-B, ACARS, IATA SSIM, OOOI timestamps, delay codes, fuel/weight & balance analytics. • - Safety programs (ASAP/SMS/LOSA), ICAO Annex 19 context; EASA/FAA exposure