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  "job_title": "Lead Data Scientist",
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  "job_country": "DE",
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  "employer_name": "IXOPAY",
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  "job_posted_at": "vor 9 Tagen",
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  "job_description": "About IXOPAY\n\nOur mission at IXOPAY is to secure and optimize payments for global commerce. We’re building an integrated platform that optimizes payment transactions and protects payments data. For merchants who understand that payments are now a strategic function, IXOPAY is a complete payments optimization platform that delivers best-in-class tokenization and transaction routing. Unlike point solutions, IXOPAY delivers omnichannel tokenization, card lifecycle management, and smart routing via any payments service provider — giving merchants unprecedented control over their revenue and the competitive edge to thrive in global commerce.\n\nWe believe our people are our most valuable asset and that our culture is defined by our core values that align the organization with our mission and strategy.\n\nPosition Overview\n\nWe are seeking a highly skilled Lead Data Scientist with a strong background in payments and data analytics to join our team. This key role will be focused on the growth of our newest IXOPAY solution, AI Payments Intelligence.\n\nIn this role, you will be part of the engineering team, working closely with sales and customers to understand their business objectives to build a product focused on data-driven solutions that enhance payment performance, cost efficiency, and data management excellence. Leveraging your deep knowledge of data science and AI, combined with payments industry expertise, you will contribute in building an AI driven payments intelligence product that allows global merchants to optimize their payment strategies, improve conversion rates, manage risks, and gain actionable insights from their data.\n\nPosition Responsibilities\n• Design, develop, and operate Ixopay’s AI Payments Intelligence capabilities, transforming complex payments acceptance and acquiring data into actionable, production-ready insights.\n• Take hands-on ownership of the full data science lifecycle: data exploration, feature engineering, model development, validation, deployment, and monitoring in live environments.\n• Build and maintain machine learning models for real-world use cases such as anomaly detection, performance degradation detection, transaction optimization, routing intelligence, and predictive analytics.\n• Develop AI-driven components, including intelligent agents, automated decision systems, and real-time or near-real-time inference pipelines.\n• Work directly with raw transaction data from PSPs, acquirers, and gateways, ensuring data quality, consistency, and reliability across ingestion and processing pipelines.\n• Collaborate closely with backend and data engineering teams to design scalable data schemas, feature stores, ETL pipelines, and model-serving infrastructure on cloud platforms.\n• Implement robust model monitoring, alerting, explainability, and drift detection to ensure reliability and trust in production systems.\n• Translate analytical findings into client-facing insights through dashboards, metrics, and visualizations in collaboration with product and frontend teams.\n• Contribute directly to architectural decisions, tooling choices, and best practices for AI and data science within the engineering organization.\n• Partner with product leadership to shape requirements, evaluate new AI features, and guide initiatives from concept to production.\n• Balance autonomy and collaboration, independently driving complex initiatives while supporting team delivery and shared technical ownership.\n• Stay current with advances in applied machine learning, AI, and data science, continuously integrating relevant techniques to strengthen the platform.\n\nPosition Qualifications\n• 7-10 years of relevant working experience\n• Graduate Degree or above in Mathematics, Physics or Computer Science with a focus in Machine Learning/Data Science\n• Familiarity with and understanding of measure theoretic probability, stochastic predictions and temporal forecasting.\n• Experience in Payments and/or Fintech in building prediction and forecasting models for KPI metrics and risk management.\n• Deep understanding of SQL and experience with monitoring tools such as Grafana, Snowflake and DBeaver.\n• Familiarity with Clickhouse, DuckDB and OLAP Databases.\n• Deep understanding of Python with focus on Data Structures & Algorithms.\n• Excellent proficiency in Pandas, Numpy, Scipy, Streamlit, Kubeflow/Vertex and Spark for data processing architectures.\n• Deep, hands-on experience with AWS is required, including S3, Lambda,\n• Experience building and deploying software with AWS, Lambdas and boto3.\n• DynamoDB, Cognito, SQS, and Bedrock.\n• Proven experience in building, deploying and owning end to end forecasting/prediction models for anomaly detection, fraud and risk with tabular Machine Learning (LightGBM, XGB, Catboost).\n• Proficiency with version control, CI/CD, Jenkins, Docker, Containerized architectures, Git/Gitlab.\n• Native English proficiency.\n• Startup experience working in a dynamic environment is preferred.\n\nBased in Munich (preferably) or elsewhere in Germany – this is a remote-friendly position, offering flexibility to work fully remotely or from our Munich location as desired.",
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  "job_employment_type": "Vollzeit",
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  "job_posted_at_timestamp": 1773273600,
  "job_posted_at_datetime_utc": "2026-03-12T00:00:00.000Z"
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