Lead Data Scientist

Key Skills

Java Programming, Design, Python Programming, Database, Framework, SQL, Git, Data science techniques, JIRA, Data Modeling, Natural Language Processing (NLP), NoSQL, PyTorch, TensorFlow, Scikit-learn, Naive Bayes

Job Description

Are you ready to take off and be part of the Allstar employee? Whether you’re applying for a developer, customer happiness or crew, at AirAsia we act as One AirAsia.

If you are hungry to make a difference with one of the most well known low-cost airlines and to work in the dynamic technology hub, this is the job for you.

The Data Science Centre of Excellence (CoE) comes under the Digital & Technology Group which is responsible for spearheading digital transformation across AirAsia. The CoE works on business and operations problems across all entities in the AirAsia Group. Key problems we solve include improving revenue and reducing costs through large-scale data federation, predictive and prescriptive analytics, state-of-the-art machine/deep learning, intelligent scheduling and optimization, and other advanced techniques.

Duties and Responsibilities: (specify day to day responsibilities)

• Improve models and algorithms to further optimize business outcomes.
• Collaborate and work across functional and multidisciplinary teams in a dynamic environment to develop an understanding of evolving/agile business needs.

Requirements and Qualifications:

• BS/MS/PhD in Science (Statistics, Management, Cognitive / Psychology, AI, Analytics, Marketing, Design, HCI).
• Up to 12 yrs relevant experience beyond first degree.
• Experience with common data science toolkits, programming languages, visualisation tools and SQL/NoSQL databases.
• Good applied statistical knowledge with emphasis in business and finance related statistical distributions, statistical testing, modeling, regression analysis, etc.
• Experience with distributed computing platforms and open-source tools and libraries.
• Experience developing and deploying to the cloud.
• Familiar or prone to adopt design thinking methods.
• Able to work under pressure and change, and balance among speed, reliability, interpretability.
• Good working knowledge of productivity tools such as G Suite, Git, Jira, Confluence.
• Experience with code versioning, code review and documentation.
• Ability to identify challenges. build relationships, and communicate across functions and geographies, and deliver successful solutions.
• Strong problem solving skills with an emphasis on business solution product development.
• Ability to mentor junior data scientists.
• Experience in one or more of the following specialized areas:

Machine Learning

• Understanding of machine learning algorithms such as k-NN, Naive Bayes, SVM, Decision trees.
• Experience using ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience with Google Cloud Platform products and services such as Vision API, Recommendations API, Cloud Natural Language.
• An in depth knowledge of AI techniques, their real-world advantages/drawbacks, and ability to prescribe and implement feasible and appropriate conventional/AI related techniques that serve as solutions to problems.

Algorithm Engineering

• Strong ability to implement, improve, and deploy ML and Math models in Golang or Python.
• Conduct systems tests for security, performance, and availability.
• Develop and maintain the design and troubleshooting/error documentation.
• Create cost effective scalable systems and develop innovative algorithm solutions.
• Research on algorithm improvements for higher performance, accuracy, and optimality.

Operations Research

• Familiar with modelling problems as mathematical programming, constraint satisfaction, particle swarm optimization and other appropriate OR methodologies.
• Familiar with tools such as Cplex, Gurobi, Google OR-Tools.
• Knowledge of advanced statistical and Operations Research techniques and concepts and experience with applications development.

Decision intelligence

• Drive decision science aspects as a standard user experience -staff or customer- process (cognitive biases, cross-cultural reasoning, statistical interpretation, human factor impact, algorithmic bias etc.).
• Actively showcase the added value of design thinking, data-driven decisions, agile and user-centric methods.
• Support research (user and markets) and data processes for enhanced decision quality.
• Develop strategic action plans integrating human factors and data science to improve AI driven decisions and choice architecture (persuasive design).