Senior Machine Learning Engineer, Zalopay

The risk management team at Zalopay consists of highly motivated team players who are dedicated to building Zalopay as the most trusted financial service and the best against bad actors in the Vietnamese fintech market. By developing and adopting advanced tech-driven prevention mechanisms/solutions while focusing on user growth and customer experience, we aim to make risk management capability a core and key differentiation of ZaloPay compared to other competitors. This helps our business grow sustainably and provide affordable services to all Vietnamese people.

How will you make an impact?

We are looking for a savvy, motivated, team-oriented experienced Machine Learning Engineer to join our Risk Data Science team. The team is responsible for bringing insights from data to assess and manage multiple financial risk exposures (e.g., promotion abuse, ATO, KYC fraud, payment fraud), as well as developing and maintaining controls, strategies, solutions, and experiences related to the end-to-end management of these exposures.

The hire will be responsible for the full life-cycle of ML/AI product (i.e., from problem identification, to data product design & development, deployment, monitoring & optimization in production) of our Risk Data Solutions & Analytics. Day-to-day duties include data analysis, monitoring and forecasting, creating the logic for and implementing risk rules and strategies; and communicating with stakeholders to ensure we deliver the best possible customer experience while meeting loss rate targets.

What will you do?

The ideal candidate needs to have a strong background in Machine Learning and Deep Learning. Also, having knowledge in Computer Vision is a plus. Be able to work with large scale of data

• Own, design, develop, and test large-scale data pipelines and algorithms that are built for speed, scale, and usability;
• Conduct end-to-end data processing, train models, troubleshooting, and problem diagnosis in the whole life cycle of AI/ML development and operation;
• Develop ML/AI backend and ensure system performances according to business requirements;
• Research and investigate academic and industrial AI/ML techniques for product improvements;
• Stay current on published state-of-the-art algorithms and competing technologies;
• Analyze and improve existing data sources, models, strategies, and metrics;
• Design and analyze experiments to pilot, test, and apply new features & solutions;
• Report, visualize, and communicate results & impacts;
• Collaborate with other engineers and team members to evaluate and improve the core components of autonomous systems;
• Identify any product/functionality/technical gaps required to deliver a solution and collaborate with internal teams to define the necessary enhancements to support delivery;
• Evaluate and recommend tools, technologies, and processes to ensure the highest quality solutions;

Foster a culture of ownership, accountability, testing, and measurement; as well as continuous improvement through mentoring, feedback, and metrics