Senior Data Scientist

How You'll Help Us Keep Climbing (Overview & Key Responsibilities)

The Predictive Engineering Data Scientist develops, trains, validates, and deploys machine learning and advanced predictive modeling solutions that identify emerging equipment degradation, predict component failures, and enable proactive maintenance actions across Delta’s fleet. Using full-flight and snapshot operational aircraft data, this role enables predictive maintenance and operational performance initiatives through scalable predictive models deployed across key platforms, such as GE EMS and Digital Alliance applications.

The successful candidate applies strong expertise in machine learning, model training and validation, feature engineering, model deployment and operationalization, anomaly detection, and predictive analytics to transform operational data into predictive models that generate actionable maintenance intelligence, enabling proactive maintenance decisions and improving fleet reliability. Working with large-scale aircraft full-flight datasets, the Machine Learning Scientist develops and continuously improves predictive models that detect early indicators of system performance degradation and potential failures before they result in operational disruptions.

This role owns the end-to-end development lifecycle of predictive machine learning solutions, from problem formulation, feature engineering, model architecture selection, algorithm evaluation, model development and optimization, training, validation through production deployment, performance monitoring, and continuous improvement. The candidate will develop production-grade ML solutions using Python, PySpark, and related technologies, ensuring models are reliable, interpretable, and integrated into Predictive Maintenance processes and daily operational decision-making.

Key Responsibilities

This role solves highly complex predictive maintenance and operational performance challenges through the application of machine learning, predictive modeling, statistical learning methods, and engineering domain expertise. The position is responsible for identifying early indicators of equipment degradation and developing predictive models that detect emerging failure conditions before they result in operational disruptions, maintenance delays, or aircraft downtime.

Many of the problems addressed by this position do not have predefined solutions. This candidate in this role must evaluate large and complex aircraft operational, flight, and maintenance datasets to identify meaningful patterns, engineer predictive features, select machine learning architectures, design and execute model experimentation, and research and develop new machine learning approaches capable of accurately modeling complex aircraft behavior and failure mechanisms. Success requires balancing predictive performance with engineering validity to ensure outputs are accurate, actionable, and operationally relevant. This role requires significant independent judgment in problem formulation, analytical methodology selection, feature engineering, model architecture design, validation strategies, and production deployment approaches.

Additionally, the position may perform advanced aircraft performance and modification studies using flight and operational data to evaluate the effectiveness of fleet initiatives and aircraft modifications. These efforts often involve complex analytical and business questions where established methodologies do not exist, requiring the creation of new data-driven modeling approaches to assess operational and financial impact.

The successful candidate must effectively collaborate with engineering and technical stakeholders, translate complex technical findings into actionable recommendations, and ensure deployed solutions deliver measurable operational value.

Machine Learning Model Development & Validation

Develop, train, validate, research, optimize, and continuously improve machine learning, predictive analytics, and anomaly detection models using full-flight and snapshot aircraft operational data. Apply statistical analysis, feature engineering, and advanced modeling techniques to identify early indicators of equipment degradation and predict component failures, and enhance predictive maintenance capabilities.

Model Deployment, Productionization & Monitoring

Productionize, deploy, monitor, and maintain machine learning models across GE EMS and Digital Alliance platforms. Ensure solutions are scalable, reliable, performant, and integrated into predictive maintenance workflows. Monitor model performance and effectiveness, and implement enhancements to improve prediction accuracy, model reliability, and operational value.

User Support, Knowledge Transfer, and Documentation

Develop and maintain technical documentation related to model design, development, validation, deployment, monitoring, and operational use. Provide knowledge transfer and technical guidance to stakeholders to support adoption, maintenance, and continuous improvement of machine learning solutions.

Visualizations and Operational Insights

Develop decision-support tools, dashboards, and visualizations that communicate model outputs, equipment health indicators, predictive insights, and operational recommendations to technical stakeholders. Transform complex machine learning results into actionable information that supports maintenance planning and operational decision-making.

Aircraft Modification & Performance Impact Analysis

Conduct analytical studies and data-driven evaluations of aircraft modifications using flight data. Assess the impact of proposed or implemented modifications on aircraft performance and operational outcomes. Develop technical recommendations and communicate findings to stakeholders, enabling informed business and engineering decisions regarding fleet investments and modification strategies.

What You Need To Succeed (Minimum Qualifications)

• Bachelor's degree in Computer Science, Applied Mathematics, Mechanical Engineering, Aerospace Engineering, Data Science, Statistics, or a related technical discipline and 4 to 7 years of relevant experience.
• Proficiency in Python and PySpark, including experience with machine learning, statistical and data processing libraries.
• Strong understanding of machine learning, statistical modeling, predictive modeling, anomaly detection, feature engineering, model validation, model development, and model production deployment methodologies.
• Experience owning the end-to-end machine learning lifecycle, including problem formulation, feature engineering, model architecture selection, design and execution of model experimentation, model development, training, validation, production deployment, monitoring and continuous improvement.
• Experience developing and deploying predictive modeling solutions and machine learning models within GE EMS, Skywise/Palantir Foundry, and other production environments.
• Experience utilizing aircraft operational, engineering, and other large datasets to develop predictive modeling solutions.
• Demonstrated project management skills with the ability to organize, prioritize, and manage multiple initiatives effectively.
• Excellent written and verbal communication skills, with the ability to convey complex technical concepts, model outputs, and recommendations to diverse audiences.
• Strong interpersonal skills and ability to collaborate effectively across teams.

Behavioral Competencies

• Ability to produce high quality results, work in a collaborative environment by embracing diverse perspectives and with a solution-based approach.
• Adapt communication clearly and concisely based on team dynamics and expresses thoughts & ideas effectively.
• Ability to engage effectively with peers and stakeholders to build trust and reliable working relationships.
• Ability to understand business processes, implement innovative solutions, guide juniors on continuous improvement by constantly updating oneself on current technology & trends.
• Inquisitive to understand customer and business expectations while creating value addition on technical solutions.

What Will Give You a Competitive Edge (Preferred Qualifications)

• Willingness to Travel