Data Scientist

**Description**: United's Digital Technology team designs, develops, and maintains massively scaling technology solutions brought to life with innovative architectures, data analytics, and digital solutions. **Our Values**: At United Airlines, we believe that inclusion propels innovation and is the foundation of all that we do. Our Shared Purpose: "Connecting people. Uniting the world." drives us to be the best airline for our employees, customers, and everyone we serve, and we can only do that with a truly diverse and inclusive workforce. Our team spans the globe and is made up of diverse individuals all working together with cutting-edge technology to build the best airline in the history of aviation. With multiple employee-run "Business Resource Group" communities and world-class benefits like health insurance, parental leave, and space available travel, United is truly a one-of-a-kind place to work that will make you feel welcome and accepted. Come join our team and help us make a positive impact on the world. **Job overview and responsibilities** The Data Scientist will work on United's predictive maintenance use cases. This will include collaboration with aircraft engineers to plan installation of new aircraft sensors and avionics, enhancements to existing aircraft monitoring software, deployment of new predictive models, collaboration with OEMs, integration of health monitoring into United's approved maintenance programs, and technology to provide aircraft data directly to technicians. - Liason with Stakeholders in the Aircraft Health Monitoring team, Aircraft Engineering and other Data Scientists to understand the importance of several Predictive Mx use case, their executive feasibility and do a cost benefit analysis to identify the highest impact use cases that need to be prioritized - Work on complex Sensor data coming out of AC systems like ACARS and QAR and be able to parse out relevant information - Analyze the AC Maintenance and Operations data that to identify issues with critical A/C systems - Build, Test, Deploy and Monitor complex Machine learning algorithms that predict AC failures - Maintain and enhance existing model deployments to make sure they stay valuable and relevant - Mentor junior members of the team and stay up-to-date with ML advances **This position is offered on local terms and conditions. Expatriate assignments and sponsorship for employment visas, even on a time-limited visa status, will not be awarded. This position is for United Airlines Business Services Pvt. Ltd - a wholly owned subsidiary of United Airlines Inc.** **Qualifications**: **Required** - Bachelor's degree in a quantitative field in quantitative field like Math, Statistics, Operations Research, Computer Science, Engineering, Physics, or related fields - At least 4 years of experience in Data Science/Machine Learning/advanced analytics/optimization - Deep understanding of data structures, relationships, and efficient transformations Familiarity with all parts of the data ecosystem - acquisition, engineering, storage, management, analysis, visualization, and model deployment and monitoring - Strong Knowledge of database querying and being able to write complex queries to extract data - Fluent skill in python/pyspark - Strong knowledge of Statistical Modelling, feature engineering, testing - Ability to propose novel modeling approaches based on the problem being solved - Must be legally authorized to work in India for any employer without sponsorship - Must be fluent in English and Hindi (written and spoken) - Successful completion of interview required to meet job qualification - Reliable, punctual attendance is an essential function of the position **Preferred** - Master's degree in a quantitative field - At least 2 years experience in a project leadership role preferred - Airline experience or knowledge of airline operations preferred - Experience with anomaly detection, imbalanced classification, and time series analysis is strongly preferred - Able to comprehend new data sources quickly and ask the right questions - Quickly get comfortable with new jargon