data analyst in payment fraud

Enfint

Barcelona

Híbrido

EUR 65.000 - 90.000

Jornada completa

Hace 13 días
Generador de candidaturas

Consigue una respuesta de este empleador — un currículum y una carta de presentación adaptados exactamente a lo que busca para contratar.

Supera los filtros ATS

Ventajas ofrecidas por este puesto de trabajo

Private insurance for employee and fam
Serenis mental health support
Stock Option Plan
Meal vouchers
Relocation support
Professional development
Internal mobility
Language courses
Unlimited PTO
Flexible working hours
3 days in-office / potential extra rem

Descripción de la vacante

Satispay ищет эксперта по рискам и платежам для построения и поддержки реальных процессов принятия решений. Ваша роль включает интеграцию ML-оценок с правилами и скоростью обработки транзакций, сотрудничество с командами продукта и инженерии, а также формирование методологий борьбы с мошенничеством и оптимизацию пользовательского опыта.

Требуется 3–7 лет опыта в риске, платежах или кредитах, сильные знания SQL и Python, владение английским языком и умение общаться на высоком уровне.

Formación

  • 3–7 лет опыта в риске, платежах или кредитах.
  • Сильные навыки SQL и Python для анализа данных и разработки стратегий.
  • Глубокое понимание динамики платежного мошенничества.
  • Опыт работы в окружении принятия решений в реальном времени.
  • Отличные коммуникативные навыки, свободно английский.

Responsabilidades

  • Архитектура и развёртывание логики принятия решений в режиме реального времени.
  • Перевод мошеннических паттернов в эффективные правила с учётом FP/FR.
  • Поиск новых мошеннических схем и разработка мер снижения риска.
  • Сотрудничество с продуктом и инженерами для определения аналитических требований.
  • Работа с дата-сиентистами для внедрения ML-моделей и правил.
  • Анализ мер безопасности и проектирование динамических стратегий friction.
  • Мониторинг эффективности правил и здоровья портфеля.
  • Презентация результатов руководству для корректировки риск-аппетита.

Conocimientos

SQL
Python
Fraud knowledge
Real-time decisioning
Communication
English

Descripción del empleo

Описание

Satispay is building a financial platform that makes financial services easy and accessible for over 6.5 million users. The company started with payments and has expanded into benefits, investing, and cards.

Задачи
  • Architect and deploy real-time decisioning logic combining ML scores with velocity features
  • Translate complex fraud patterns into high-performing rules while balancing False Positive Rates and Gross Fraud Loss
  • Hunt for emerging fraud schemes, including ATO and social engineering, and develop end-to-end mitigation strategies
  • Partner with Product and Engineering to define analytical requirements for new features and third-party data signals
  • Work with Data Science to operationalise machine learning models and combine model scores with heuristic rules
  • Analyse security measures and design dynamic friction strategies for a seamless user experience
  • Monitor rule efficacy and portfolio health
  • Present data-driven recommendations to senior leadership to evolve risk appetite
Требования
  • 3-7 Years of professional experience in risk, payments, or credit
  • Strong SQL and Python skills for data analysis and strategy development
  • Solid understanding of payment fraud dynamics
  • Experience working with real-time decisioning environments
  • Excellent communication skills
  • Fluent English
  • Nice to have: Experience with Machine Learning, Data Science, PySpark, or GraphQL knowledge
Условия
  • Private insurance for the employee and family
  • Psychological support with Serenis and mental health workshops
  • Stock Option Plan
  • Meal vouchers
  • Relocation support if moving countries
  • Professional development programs
  • Internal mobility
  • Language courses with Preply
  • Unlimited PTO
  • Flexible working hours
  • Three days per week in-office, with the option to request extra remote time
  • Enhanced parental leave
  • Additional leave for child sickness
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