Lead Data Scientist (Customer Intelligence)

Key Skills

Java Programming, JQuery, Python Programming, Database, SQL, Git, Machine learning techniques, Data science techniques, JIRA, Deep Learning, NoSQL, Pandas, PyTorch, TensorFlow, Kubernetes-K8s, Neo4J, Scikit-learn, Naive Bayes, Data Modelling

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.

Group Data comes under AirAsia Digital which is responsible for spearheading digital transformation across AirAsia. Group Data 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. Group Data is also responsible for the data lakes across all our businesses, deriving insights and value from them and sharing them back with the businesses. In addition, Group Data also actively participates in innovation and training in the Redbeat Academy as well as collaboration with strategic partners like Google, GE, Airbus, and academia.

What You'll Do:

• Develop, train, implement models that significantly impact the field of data science and data analytics in Customer Intelligence and other areas.
• Design and develop data-related prototypes, POCs, programs, applications, reports, dashboards, etc., and deliver solutions that directly provide value to the business.
• Improve models and algorithms to further optimize business outcomes, especially in the area of Customer Intelligence.
• Collaborate and work across functional and multidisciplinary teams (including Data Scientists, Data Engineers, Software Developers, various stakeholders, etc.) in a dynamic environment to develop an understanding of evolving/agile business needs.
• Formulate hypotheses, and design and develop corresponding necessary measures, systems, simulations, and experiments in order to validate the hypotheses.

About You:

• BS/MS/PhD in Computer Science, Data Science, IT, Statistics, AI, Machine Learning, Analytics, etc.
• At least 5 years of relevant experience beyond the first degree. 
• Must have strong Data Science skills, knowledge and experience, and strong domain knowledge in Customer Intelligence, Business Intelligence in Consumer Experience, etc. Must have successfully completed projects in large-scale real-world Customer Intelligence.
• Customer Profile, Segmentation, feature store, CLTV modelling, customer lifecycle optimization, CRM, etc.
• High proficiency in SQL, and experience with NoSQL databases.
• Good applied statistical knowledge with emphasis in business, finance and Customer Intelligence related statistical distributions, statistical testing, modeling, regression analysis, etc.
• Understanding of machine learning algorithms such as k-NN, Naive Bayes, SVM, Decision tree, and different kinds of Machine Learning objectives such as supervised learning (classification, regression, etc.), unsupervised learning, semi-supervised learning and reinforcement learning.
• Have completed successful large-scale projects using Python and its corresponding Data Science, Machine Learning and Deep Learning ecosystem of libraries or frameworks including Scikit-Learn, Pandas, TensorFlow, PyTorch, etc.
• Experience with code versioning, code review and documentation.
• Experience using cloud platform products and services.
• Good working knowledge of productivity tools such as G Suite, Git, Jira, Confluence.

Good to have:

• Experience writing clean and well-engineered code for containerised applications to handover to other teams.
• Experience using Google Cloud Platform products and services (such as BigQuery and App Engine, AI Platform, Cloud Big Table, Cloud Firestore, Cloud Run and Kubernetes Engine) and Google Data Studio. 
• Graph modelling and Graph Query Language. E.g. Neo4j and Cypher Query Language.
• Product (front-facing) experimentation experience.