Data Scientist - Payments
About IXOPAY
IXOPAY is the enterprise-grade global payment infrastructure platform built for the era of agentic commerce. We equip merchants and enterprises with AI-driven intelligence, payment orchestration, advanced tokenization, and the tools to optimize every stage of the payments journey.
From intelligent routing and compliance to customizable modules and enterprise-scale orchestration, IXOPAY helps businesses integrate faster, improve payment performance, and expand globally with confidence.
At IXOPAY , our people are our greatest strength. Our collaborative culture is built on shared values that guide how we work, innovate, and support our customers.
About the Role
We're looking for a Data Scientist - Payments to help shape the future of IXOPAY 's AI Payments Intelligence platform.
As part of our Data Science & Engineering team, you'll develop machine learning models and AI-driven capabilities that transform complex payment data into actionable insights. Working closely with Engineering, Product, and customers, you'll build solutions that help global merchants optimize payment performance, improve conversion rates, detect anomalies, and manage risk.
This is a hands-on role with ownership across the full data science lifecycle—from research and model development through production deployment and ongoing optimization.
What You'll Do
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Design, build, deploy, and maintain machine learning models that improve payment performance and operational efficiency.
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Develop AI-driven capabilities including anomaly detection, predictive analytics, intelligent routing, and automated decision-making.
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Own the end-to-end data science lifecycle, including data exploration, feature engineering, model validation, deployment, and monitoring.
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Work with large-scale payment transaction data from PSPs, acquirers, and gateways to deliver reliable, production-ready insights.
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Collaborate with Data Engineering and Backend teams to build scalable data pipelines, feature stores, and model-serving infrastructure.
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Monitor model performance, explainability, and drift to ensure reliable production systems.
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Partner with Product and Engineering teams to define new AI capabilities and bring them from concept to production.
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Contribute to data science best practices, technical standards, and the evolution of our AI platform.
What You'll Bring
Required
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3+ years of experience in Data Science, Machine Learning, or Applied AI.
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Strong Python skills with experience building production machine learning solutions.
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Proven experience developing, deploying, and maintaining predictive models for anomaly detection, forecasting, optimization, or risk management.
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Experience with modern machine learning frameworks and libraries such as Pandas, NumPy, SciPy, and gradient boosting frameworks (e.g. LightGBM, XGBoost, or CatBoost).
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Strong understanding of statistical modelling, forecasting, and data analysis.
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Experience working with SQL and large-scale analytical databases.
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Experience collaborating within cross-functional engineering teams to deliver production AI solutions.
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Excellent communication skills and the ability to explain complex analytical concepts to technical and non-technical stakeholders.
Preferred
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Experience in payments, FinTech, fraud prevention, or financial services.
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Advanced degree in Computer Science, Mathematics, Physics, Data Science, or a related quantitative discipline.
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Experience with ClickHouse, DuckDB, Spark, Snowflake, or similar analytical platforms.
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Experience with cloud platforms such as AWS (including S3, Bedrock, Lambda, DynamoDB, or Cognito).
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Familiarity with Kubeflow, Vertex AI, Docker, CI/CD pipelines, and containerized deployment.
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Experience with data visualization and monitoring tools such as Grafana or Streamlit.
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Experience working in a startup or high-growth technology environment.
Location
This is a remote-friendly position based in G