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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?
The hire will be responsible for the full life- cycle of data 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.
We are looking for a savvy, motivated, team- oriented Data Scientist 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.
What will you do?
The ideal candidate is an experienced solution and team builder who enjoys optimizing complicated problems or building them from the ground up. The data science team will work closely with our risk pillar owners, data engineers, and software engineers on new mechanism/solution initiatives and will ensure optimal data/model/system architecture is consistent throughout ongoing projects. The candidate must be self- directed and comfortable supporting the needs of multiple teams, systems, and products.
Foster a culture of ownership, accountability, testing, and measurement; as well as continuous improvement through mentoring, feedback, and metrics
Analyze and improve existing data sources, models, strategies, and metrics;
Evaluate and recommend tools, technologies, and processes to ensure the highest quality solutions;
Conduct end- to- end data processing, troubleshooting, and problem diagnosis in the whole life cycle of AI/ML development and operation;
Design and analyze experiments to pilot, test, and apply new features & solutions;
Focus on bringing statistical depth, analytical insights, and accurate interpretation of data;
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;
Extract, analyze, and apply data- mining/AI/ML techniques to large structured and unstructured datasets;
Research and investigate academic and industrial AI/ML techniques for product improvements;
Own, design, develop, and test large- scale data science pipelines and algorithms that are built for speed, scale, and usability;
Stay current on published state- of- the- art algorithms and competing technologies;
Report, visualize, and communicate results & impacts;
