Reporting Developer Senior- Data Scientist Senior

Reporting Developer Senior – Data Scientist Senior

Location:

GAC Savannah.

Data and analytics development within the Gulfstream Business Technology organization, supporting the Enterprise Reporting team. Responsibilities include developing, testing, deploying, and supporting reporting and data analytics solutions.

• Minimum 5 years experience in Data Science
• Expert Python for ML and data engineering; modular coding, packaging, unit testing (pytest)
• Hands‑on experience with Databricks (Azure), Microsoft Fabric, or AWS Sage Maker;
  Lakehouse/warehouse patterns, Spark Data Frames, Delta Lake (ACID, OPTIMIZE/VACUUM), MLflow for experiment tracking, model registry, and jobs/workflows
• ML tooling: scikit‑learn, XGBoost/Light
  
  GBM, stats models; feature engineering at scale; model selection, cross‑validation, hyperparameter tuning; robust performance diagnostics (ROC/AUC, PR, F1, lift, residual/error analysis)
• MLOps: MLflow, model packaging, batch/stream inference patterns on Databricks, job orchestration, CI/CD for notebooks/repos, environment reproducibility (conda/poetry), model monitoring (drift, stability, recalibration triggers)

• Classical ML focus (non‑GenAI):
  Time series forecasting (ARIMA/SARIMA/ETS/Prophet, gradient boosting for forecast blending), supervised learning, unsupervised learning, survival/reliability analysis, design of experiments (DOE)
• Feature engineering:
  Lag/lead features, calendar/maintenance cycles, sensor aggregation windows, rolling statistics, missingness/outlier treatment, target‑leakage controls, robust scaling for heteroscedastic industrial data; synthetic data generation
• Evaluation in production contexts:
  Backtesting strategies, cross‑site validation (multiplant/multiaircraft), cost‑sensitive metrics (false‑alarm vs. miss costs), explainability for engineering review boards; optimization of Type 1 vs. Type 2 errors

• Industrial analytics:
  Predictive maintenance, condition‑based monitoring, yield/throughput improvement, quality/defect analytics, SPC, first‑pass yield, takt‑time bottleneck analysis in complex discrete manufacturing
• Aerospace value chain:
  Practical familiarity with end‑to‑end flows—design, supply chain, assembly, test/flight, field operations & MRO; interpretation of sensor/telemetry (AHTMS), maintenance logs, and part lifecycle histories
• ERP/MES/PLM context:
  Working knowledge of SAP/ERP data constructs (orders, routings, WIP, inventory), MES event logs, and engineering change impacts on data semantics

• Stakeholder engagement:
  Translate business problems into ML formulations, quantify expected value, define acceptance criteria, present results to engineering, reliability, and operations leaders
• Cross‑functional teamwork:
  Close collaboration with Data Engineering (ETL, CDC, quality, security) and Citizen Data Scientists for enablement and review cycles
• Documentation & training:
  Produce clear technical docs, model cards, operational runbooks; coach junior DS and citizen developers

Bachelor’s degree in Information Technology, Computer Science, Engineering, or related field; 9 years of relevant business/technical experience, including five (5) years in reporting, data analytics, or related technical development/deployment. Master’s may offset one (1) year, PhD may offset two (2) years.