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Posted on 2026/10/29

AI Data Scientist

Fiserv

Pacific, WA, United States

Full-time

Overview

In this role you will design and deliver advanced AI/ML and generative AI solutions for high-volume fintech use cases, spanning fraud, risk, forecasting, and payment optimization.

You will own the end-to-end data science lifecycle and partner across product, engineering, and business teams to move research into production.

You’ll build scalable pipelines on cloud/MLOps platforms, ensure data integrity and responsible AI, and mentor peers. This is a hands-on role with a global impact on payments and financial services.

Responsibilities

  • Design, develop, and deploy AI/ML solutions for complex business problems in payments and financial services
  • Own problem formulation, data exploration, feature engineering, experimentation, and deployment
  • Apply AI to merchant/consumer intelligence, fraud/risk, forecasting, anomaly detection, and optimization
  • Collaborate with cross-functional teams and mentor data scientists
  • Develop scalable AI/ML pipelines with testing, observability, governance, and performance optimization
  • Create training/evaluation datasets while ensuring data privacy and responsible AI practices
  • Develop domain-specific models via prompt/context engineering, fine-tuning, and model evaluation
  • Build generative and agentic AI solutions including foundation-model applications and multi-agent systems

Key requirements

  • 5+ years delivering high-impact AI solutions in tech/fintech/financial services
  • 2+ years developing LLM/generative AI solutions including autonomous agents and RAG pipelines
  • Production-grade AI/ML experience on Azure, AWS, Databricks, Snowflake or similar
  • Strong Python and SQL with PyTorch, scikit-learn, XGBoost, Spark experience
  • Deep expertise in ML/DL, forecasting, anomaly detection, optimization, model validation
  • Fintech/payments domain knowledge and handling high-volume transactional data
  • Bachelor's in Computer Science, AI, Data Science or related quantitative field
  • Cross-functional collaboration
  • Mentoring and leadership
  • Communication of complex AI concepts to non-technical stakeholders
  • Python
  • SQL
  • PyTorch