Senior AI Data Scientist
About The Position
The Senior AI Data Scientist designs and delivers 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 and areas including merchant and consumer intelligence, fraud and risk, forecasting, and payment optimization. The position partners 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, production-ready AI/ML pipelines and generative or agentic AI applications. It also translates emerging AI research into reusable capabilities, mentors data scientists, and ensures solutions meet standards for testing, observability, governance, performance, data integrity, privacy, security, and responsible AI. This is an on-site position, Monday through Friday, with approximately 10% off-site or inter-office travel expected. Candidates must currently have valid and unrestricted U.S. work authorization; sponsorship is not available.
Requirements
- 8+ years of experience delivering high-impact AI solutions in technology, fintech, payments, financial services, research, or consulting organizations.
- 3+ 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, with experience using frameworks such as PyTorch, scikit-learn, XGBoost, and Spark to work with large-scale datasets.
- Deep applied AI expertise in machine learning, deep learning, statistical modeling, experimentation, forecasting, anomaly detection, optimization, and model validation.
- Strong fintech, payments, banking, or financial-services experience, including practical knowledge of high-volume transactional data and financial-data controls.
- Master's or PhD in Computer Science, Artificial Intelligence, Data Science, or another quantitative field from a recognized institution.
- Currently possess valid and unrestricted U.S. work authorization; candidates requiring sponsorship now or in the future will not be considered.
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 for complex business problems 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 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, including appropriate testing, observability, governance, and performance optimization.
- Create 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.
Benefits
- Annual incentive opportunity for incentive-eligible associates, which may be delivered as a mix of cash bonus and equity awards at the Company's discretion.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
