Assistant Manager- Machine Learning & Operations Research

Job Description

At Indigo, we are dedicated to achieving operational excellence and the highest standards of safety and efficiency in our Flight Operations. We leverage data-driven insights and advanced analytics to increase crew productivity, improve fuel efficiency, enhance scheduling, and ensure compliance with aviation regulations. Our Flight Operations Analytics team plays a critical role in supporting these objectives by providing actionable insights, analytical tools, and streamlined processes that inform our operational strategies. Our mission is to deliver reliable, integrated, and actionable data-driven insights that enable us to surpass our operational goals.

Preferred Skills and Qualifications:

Strong foundation in machine learning, data science, and research methodologies.
Proficiency in programming languages such as Python or R for data analysis and model development.
Comfortable with visualization tools like PowerBI and query languages such as SQL.
Solid understanding of statistical analysis and experimental design.
Familiarity with aviation operations or a strong willingness to learn the nuances of the industry.
Excellent communication and presentation skills.
Collaborative mindset and the ability to work effectively in a team-oriented environment.
 

Responsibilities
Machine Learning & Research:
Develop and implement machine learning models to predict operational challenges and optimize processes.
Conduct research to identify cutting-edge methodologies and tools in data science and machine learning relevant to aviation.
Experiment with advanced algorithms for predictive modeling, anomaly detection, and optimization.
Collaborate on designing and deploying scalable machine learning pipelines for real-time decision-making.

Data Analysis & Insights:
Analyze flight data to uncover patterns and trends that enhance crew productivity, fuel efficiency, and scheduling.
Generate predictive insights to proactively address operational inefficiencies.
Leverage statistical methods and machine learning techniques to provide actionable insights.

Visualization & Communication:
Create interactive dashboards and visualizations using tools like PowerBI and SQL to present data in a compelling and actionable manner.
Effectively communicate insights and research findings to diverse stakeholders, including technical and non-technical audiences.

Innovation & Experimentation:
Lead experimentation initiatives to test new operational strategies or process improvements.
Evaluate the effectiveness of experiments through robust measurement frameworks and statistical analysis.
Stay updated on industry trends and emerging technologies to incorporate innovative solutions into operations.
 

Strategic Collaboration:
Partner with cross-functional teams, including pilots, operations managers, and maintenance teams, to identify data-driven solutions to operational challenges.
Support leadership with insights to drive strategic decisions and long-term planning.

Regulatory Compliance:
Analyze and model data to ensure compliance with aviation safety standards and regulations.
Support audit and certification processes with accurate and detailed analytical insights.
Optimization & Process Improvement:
Identify opportunities for operational efficiency through data-driven approaches.
Propose and implement solutions to streamline processes, reduce costs, and improve performance metrics.
 

Competencies