15 hours ago
Atlanta, GA, USAMid Level
Responsibilities
- Build and improve model integration pipelines by automating data workflows and enabling efficient data integration.
- Support global partners in retrieving aircraft parameters and logbooks and integrating predictive maintenance models into Skywise.
- Transform disparate datasets into high-signal models through automated data pipelines.
- Research R&D topics and propose AI/ML opportunities and technical architectures to improve scalability and cost.
- Collaborate with international partners and managers through weekly discussions and occasional travel.
- Contribute to technical discussions, documentation, research, innovation, and sustainability initiatives.
Requirements
- Understand basic aircraft systems, aircraft-generated parameters, and temperature, vibration, and pressure sensors used for flight-health monitoring.
- Have basic coding experience, preferably with Python, SQL, and Regex, or an MS equivalent.
- Have a data-oriented analytical mindset and enjoy investigating the meaning and source of datasets.
- Be willing to learn Palantir Foundry, Apache Spark/PySpark, and specialized Python frameworks used by the Skywise platform.
- Communicate technical solutions effectively in English with engineers and non-technical stakeholders.
- Hold a bachelor’s or master’s degree in Computer Science, Data Science, or a related field, or have equivalent professional experience.
- Have at least two years of experience or an MS equivalent.
- Be able to independently research R&D topics and propose new opportunities and solutions.
Benefits
- Permanent employment with flexible work arrangements and flexible hours.
- Vacation days, Christmas shutdown, 12 sick days, and summer core hours.
- Training and development support, monthly Lunch and Learns, and a referral program.
- Competitive group benefits, maternity and paternity benefits, mental health support, pension scheme, and employee share scheme.
- Work travel opportunities, casual dress code, work-life balance initiatives, rewards and recognition, and inclusion and diversity programs.
Tech Stack
Categories
Data Engineering
