Top Data Science Jobs in Malaysia

Own the product vision, roadmap, backlog, prioritization, and release planning for AirAsia’s digital customer platforms. Partner with stakeholders and engineering teams to deliver booking, check-in, chatbot, payments, ancillary, and communications capabilities. Lead Agile teams, define requirements, improve customer experience, analyze markets and competitors, and prioritize high-value initiatives. The role requires product management and software engineering experience, e-commerce expertise, technical understanding of APIs, microservices, cloud platforms, and strong analytical, communication, and leadership skills.

Own product vision, roadmap, backlog prioritization, requirements, release planning, and Agile delivery for AirAsia customer and merchant support platforms. Partner with stakeholders and engineering teams to improve digital experiences, develop product features, manage sprint and release goals, analyze users and markets, and identify growth opportunities. The role requires product management experience, e-commerce exposure, technical understanding of APIs, microservices, cloud platforms, analytics, and strong leadership and communication skills.

Develop and improve data science and machine learning models for marketing and consumer insights, including attribution, personalization, forecasting, experimentation, and marketing measurement. Prepare datasets, define evaluation metrics, support model deployment, and generate insights from media, pricing, promotions, and syndicated data. Collaborate with product owners, data engineers, and machine learning engineers; contribute to responsible AI governance, code reviews, reusable AI assets, and mentorship of junior team members.

The intern will lead digital transformation projects, analyze data using Power BI and Excel, assist in application deployments, and generate reports.

The internship involves developing and optimizing digital systems, analyzing data for insights, and supporting operational efficiency through technology solutions.

The Data Scientist leads analytics initiatives for credit card portfolios, utilizing statistical models and machine learning to drive business growth and optimize risk management.

Lead and develop a Data Science and AI team while owning the advanced analytics roadmap and delivery portfolio. Guide predictive analytics, machine learning, Generative AI, and Agentic AI products from discovery through production, adoption, monitoring, and continuous improvement. Partner with technology, cybersecurity, risk, compliance, vendors, and senior executives to ensure responsible governance, reliable operations, measurable business outcomes, and sustainable adoption.

Lead end-to-end technical recruitment globally across engineering, product, data, AI/ML, infrastructure, DevOps, and cybersecurity. Partner with senior stakeholders on hiring strategy, build talent pipelines and market intelligence, mentor junior recruiters, and improve recruiting processes, ATS data, and recruiting metrics to deliver high-quality hires across regions.

Support DataX initiative for Exploration & Development Division by assisting in data analysis for UDP adoption and AI/ML features.

Supports data science and analytics solution development that drives business growth, customer experience, productivity, and efficiency. Responsibilities include delivering analytics workplans, developing statistical, predictive, segmentation, and propensity models, creating reusable best practices, supporting business decisions, and collaborating with multidisciplinary teams. The role requires programming-based analytics expertise, machine learning knowledge, strong analytical and problem-solving skills, business acumen, and project management capabilities.

Support development of an automated Off-hold Tracking solution: extract and clean FQAP data, build simple reports/dashboards, validate outputs against manual records, document work, and present progress in team meetings.

Lead strategy and execution of data analytics and data science across the organisation: build and mentor teams, partner with cross-functional stakeholders, drive data-driven initiatives that improve risk management and business metrics, uplift tooling and capabilities, and ensure data governance and regulatory compliance across markets.