Data Scientist II

Job Description

Asia Digital Engineering (ADE) is transforming aircraft maintenance through data and innovation. As a wholly-owned subsidiary of Capital A Berhad, we're building the future of MRO by combining AirAsia Group Engineering's expertise with cutting-edge data science and technology.

We're seeking a Data Scientist II to join our team in developing data products and AI solutions that directly impact aircraft safety, operational efficiency, and maintenance optimization. You'll work with diverse datasets from Airbus A320, A321 & A330 fleet operations to uncover insights that shape how modern aircraft are maintained and serviced.

This role offers the opportunity to apply data science in a high-stakes, highly regulated industry where your models and analyses directly contribute to aviation safety and operational excellence.

• Conduct exploratory data analysis (EDA) to uncover patterns, anomalies, and trends in aircraft operational and maintenance data.

• Design and maintain robust ETL pipelines that ensure data freshness, completeness, and integrity across multiple aviation data sources.

• Build end-to-end data products from development through deployment, including monitoring and ensuring system reliability in production.

• Develop and implement machine learning models (regression, classification, clustering) to predict maintenance needs and optimize operations.

• Leverage Large Language Models (LLMs) to enhance data products through automated feature extraction, data enrichment, and intelligent information retrieval and decision making.

• Create scalable data solutions that can handle real-time aircraft sensor data and maintenance logs.

• Deploy models and data pipelines to cloud infrastructure (GCP), implementing proper monitoring, alerting, and retraining workflows.

• Build intelligent automation systems that can process unstructured maintenance reports and technical documentation.

• Ensure data product reliability through robust error handling, logging, and performance optimization.

• Create clear data visualizations and communicate insights to technical teams, engineers, and management stakeholders.

• Collaborate with aviation engineers and operations teams to understand requirements and translate them into scalable analytical solutions.

• Maintain production systems including troubleshooting issues, optimizing performance, and ensuring uptime.

• Participate in code reviews and maintain comprehensive documentation for reproducibility and compliance.

• BS/MS in Computer Science, Statistics, Mathematics, Engineering, or related field.

• 2-4 years of hands-on experience in data science and/or data engineering.

• Strong proficiency in Python and SQL, with experience building production-grade code. (We embrace AI-assisted coding tools to enhance productivity, but believe they work best in the hands of engineers who deeply understand code structure, debugging, and system design.)

• Solid foundation in statistics and machine learning concepts with practical implementation experience.

• Experience building and deploying data pipelines and ML models to production environments.

• Hands-on experience with cloud platforms (preferably GCP) including compute, storage, and ML services.

• Experience with software engineering practices: version control (Git), CI/CD, testing, and monitoring.

• Strong problem-solving skills with the ability to work independently on end-to-end solutions.

• Excellent communication skills to collaborate with technical and non-technical stakeholders.

• Experience with distributed computing and handling large-scale datasets

• Familiarity with NoSQL or Graph databases

• Experience in aviation, manufacturing, or other industrial domain

• Mandarin speaker will have an added advantage

We work across these domains and welcome candidates with experience or strong interest in one or more of these areas:

Data Engineering for ML

• Building scalable data pipelines. (Airflow)

• Working with streaming data and real-time processing. (Dataflow, Pub/Sub)

• Model serving and API development. (FastAPI)

• Data quality and monitoring frameworks.

Machine Learning

• Classical ML algorithms and frameworks. (scikit-learn, TensorFlow, PyTorch)

• Model evaluation, feature engineering, and deployment.

• Cloud-based ML services. (especially GCP)

Modern AI & LLM Applications

• Building applications with Large Language Models.

• RAG systems and vector databases.

• Prompt engineering, auto prompt optimizers (like DSPy) and LLM evaluation.

• Experience with frameworks like Agents SDK or similar.

Operations Research

• Optimization problems in scheduling and resource allocation.

• Experience with optimization tools (OR-Tools, Gurobi).

• Applied problem-solving in operational contexts.