AI Data Scientist

Fiserv•Columbia, WA

11d•Onsite

About The Position

The AI Data Scientist will design and deliver advanced applied AI, machine learning, and generative AI solutions for complex fintech and payments use cases. This hands-on role works with high-volume transaction data, merchant and consumer intelligence, fraud and risk, forecasting, and payment optimization, partnering with product, engineering, architecture, and business teams to move solutions from research into production. The role owns the end-to-end data science lifecycle and develops scalable AI/ML pipelines and production solutions using modern cloud, data, and MLOps platforms. It also develops domain-specific and generative AI solutions, including foundation-model applications, retrieval-augmented generation, tool-calling workflows, structured outputs, and multi-agent systems. Solutions should incorporate appropriate testing, observability, governance, performance optimization, data integrity, privacy, security, and responsible AI practices. This position is on-site Monday through Friday, with approximately 10% travel off-site or to other office locations. Candidates must currently have valid and unrestricted U.S. work authorization; candidates requiring sponsorship now or in the future will not be considered. The stated salary range is $90,000 to $152,400 for employees in Massachusetts, California (excluding Sunnyvale), and the District of Columbia; ranges may differ in other states. Incentive-eligible associates may receive an annual incentive opportunity, potentially as a mix of cash bonus and equity awards.

Requirements

5+ years of experience delivering high-impact AI solutions in technology, fintech, payments, financial services, research, or consulting organizations.
2+ years of hands-on experience developing LLM and generative AI solutions, including autonomous agents, foundation-model architectures, and production-grade RAG pipelines.
Experience building production-grade AI/ML solutions using cloud and data platforms such as Azure, AWS, Databricks, Snowflake, or comparable technologies.
Strong programming skills in Python and SQL.
Experience using frameworks such as PyTorch, scikit-learn, XGBoost, and Spark to work with large-scale datasets.
Deep applied AI expertise across machine learning, deep learning, statistical modeling, experimentation, forecasting, anomaly detection, optimization, and model validation.
Strong fintech, payments, banking, or financial-services experience, with practical knowledge of high-volume transactional data and financial-data controls.
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or another quantitative field from a recognized institution.
Must currently possess valid and unrestricted U.S. work authorization; candidates requiring sponsorship now or in the future are not eligible.

Nice To Haves

Experience with card payments, acquiring, issuing, merchant analytics, fraud, risk, authorization, digital payments, or payment-network data.
Experience adapting and optimizing foundation models using fine-tuning, LoRA/QLoRA, distillation, or comparable techniques.
Demonstrated ability to mentor data scientists and establish reusable AI patterns, engineering standards, and responsible AI practices.
Experience with vector and hybrid retrieval, semantic data architectures, model/agent observability, MLflow, and automated evaluation frameworks.
Experience with agent orchestration frameworks such as LangGraph, Microsoft Agent Framework, LlamaIndex, OpenAI Agents SDK, or comparable technologies.

Responsibilities

Design, develop, and deploy AI and machine learning solutions that address complex business problems and create measurable value across payments and financial services.
Own the end-to-end data science lifecycle, including problem formulation, exploratory analysis, feature engineering, experimentation, model development, validation, deployment, monitoring, and continuous improvement.
Apply advanced analytics and AI to payment and transaction use cases such as merchant and consumer intelligence, fraud and risk, personalization, forecasting, anomaly detection, and authorization optimization.
Partner with cross-functional teams, mentor data scientists, and translate emerging AI research into reusable, production-ready capabilities.
Develop scalable AI/ML pipelines and production solutions using modern cloud, data, and MLOps platforms, with appropriate testing, observability, governance, and performance optimization.
Create high-quality training, evaluation, and synthetic datasets while maintaining data integrity, privacy, security, and responsible AI practices.
Develop and adapt domain-specific models through prompt and context engineering, retrieval, fine-tuning, model optimization, and rigorous evaluation.
Build generative and agentic AI solutions, including foundation-model applications, retrieval-augmented generation, tool-calling workflows, structured outputs, and multi-agent systems.