Machine Learning Scientist, Aviation

About ZestIOT

ZestIOT is a leader in aviation technology, delivering AI/ML-driven solutions that enhance operational efficiency and predictability. We are seeking a skilled Data Scientist to join our team and develop advanced machine learning models that improve turnaround accuracy and resource planning. Role Overview This role focuses on building predictive models for POBT (Predicted Off-Block Time) and ARDT (Actual Ready Time), as well as designing LSTM-based time-series models to forecast critical aviation events. The ideal candidate will combine strong technical expertise with domain awareness to deliver actionable insights that drive operational excellence. Key Responsibilities Develop and deploy ML models for POBT and ARDT prediction. Design and optimize LSTM models for time-series forecasting in aviation operations. Collect, preprocess, and validate aviation datasets including flight schedules, ground operations, and sensor data. Collaborate with engineering and operations teams to integrate models into production pipelines. Monitor model performance and retrain with updated data to ensure accuracy and robustness. Communicate insights through dashboards, reports, and stakeholder presentations. Document model design, data pipelines, and evaluation results for reproducibility. Qualifications & Requirements Education Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related field. Experience 2.5+ years of hands-on experience in data science with a proven track record in predictive modelling, machine learning, and time-series forecasting. Technical Skills Strong proficiency in Python and core ML libraries: NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch. Solid understanding of supervised learning algorithms including Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM). Hands-on experience with time-series modeling and LSTM architectures. Proficiency in SQL and experience with data pipeline frameworks. Familiarity with cloud platforms, preferably AWS. Strong analytical thinking and ability to communicate technical findings to non-technical stakeholders. Preferred / Nice-to-Have Skills Experience with aviation datasets and operational metrics. Knowledge of MLOps practices for scalable model deployment and monitoring. Exposure to big data tools such as Apache Spark or Hadoop. Experience with experiment tracking tools (e.G., MLflow, Weights & Biases). What We Offer Opportunity to work on cutting-edge aviation ML solutions with real-world global impact. Collaborative environment with technical leaders in aviation and cloud infrastructure. Access to modern infrastructure including GPU workstations and cloud-native tools. Professional growth through mentorship, training, and exposure to aviation domain challenges.