Влиятельная роль уровня Staff в глобальном финтех-единороге с фокусом на передовой ML и очень четкими ожиданиями.
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Передовой стек
Трендовый домен
Высокий потенциал роста
Прозрачные задачи
Мэтч & Сопровод
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Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
Staff Data Scientist, Algorithm (Payments Fraud & Optimization) (Payments/AI): Designing and implementing advanced algorithms to optimize payment conversion, fraud risk, and transaction costs with an accent on contextual bandits, experimentation, and causal inference. Focus on deploying ML systems to production, solving complex optimization problems at scale, and influencing product strategy through technical leadership.
Location: Singapore
Company
Airwallex provides an AI-native financial operating system and infrastructure for global payments and financial products.
What you will do
Define the technical strategy for multi-objective payments optimization across conversion rates, fraud risk, and transaction costs.
Own complex, cross-functional initiatives with broad business impact.
Provide technical leadership on optimization algorithms, contextual bandits, A/B testing, and causal inference.
Collaborate with engineering to build optimization infrastructure and deploy ML systems to production.
Influence product strategy and investment decisions through data insights and technical recommendations.
Mentor junior data scientists and engineers.
Requirements
Advanced degree in statistics, engineering, computer science, economics, or a related quantitative field.
5+ years of industry experience in data science or applied machine learning.
Proven experience with experimentation and causal inference for product decisions.
Technical leadership experience defining critical systems and influencing product strategy.
Proficiency in SQL and Python or R.
Experience using LLMs, AI, and agents to improve processes and ML models.
Nice to have
Strong experience deploying ML systems to production.
Culture & Benefits
Work on payments optimization problems with direct business impact.
Collaborate with cross-functional teams in a fast-paced, high-growth environment.
Drive adoption of advanced data science and experimentation techniques.
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