Our client is redefining payment processing with AI-driven routing, cascading transaction logic, and real-time risk assessment—designed to maximize revenue, increase approval rates, and optimize transaction flows.
They are looking for a Senior Data Scientist to develop and deploy advanced AI models that enhance payment success rates, dynamically optimize revenue, and mitigate fraud. This role requires expertise in machine learning, predictive analytics, and financial risk modeling, with a focus on real-time decision-making at scale.
This is an opportunity to work end-to-end on cutting-edge AI/ML solutions, leveraging large transaction datasets to power intelligent payment orchestration, revenue-driven routing strategies, and fraud prevention models.
Job Responsibilities
- Develop AI-powered payment routing models that dynamically select the best payment path, balancing approval rates, transaction fees, and risk to optimize total revenue.
- Design and implement adaptive cascading logic and retry strategies based on issuer behavior, transaction success patterns, and PSP performance to recover failed payments.
- Build and deploy real-time risk assessment models to detect fraudulent activity while minimizing false declines and maintaining a frictionless payment experience.
- Use predictive analytics to model transaction profitability, routing efficiency, and PSP cost structures, providing insights to refine business strategy.
- Deploy and scale machine learning models for low-latency decision-making, handling millions of transactions daily.
- Extract insights from large-scale transactional data to uncover patterns, improve payment performance, and drive revenue growth.
- Work closely with product, engineering, and business teams to align AI-driven strategies with real-world payment challenges and ensure business impact.
Requirements
- 5+ years of experience in data science, machine learning, or AI research, preferably in payments, fintech, or financial analytics.
- Strong programming skills in Python, SQL, and machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Hands-on experience with real-time ML model deployment and optimization.
- Deep understanding of payment transaction flows, PSP integrations, and revenue-driven optimization techniques.
- Experience with big data processing (Spark, Kafka, Databricks, or similar).
- Proven ability to apply probabilistic modeling, anomaly detection, and A/B testing in large-scale financial systems.
- Strong analytical and problem-solving skills, with the ability to translate data insights into actionable business decisions.
Nice to Have
- Experience with AWS cloud computing for ML model deployment.
- Background in real-time bidding (RTB), credit scoring, or financial risk analytics.
- Knowledge of PSP cost structures, chargeback mitigation strategies, and alternative payment methods.
