AI-Data Science, Advisor
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 across high-volume transaction data, merchant and consumer intelligence, fraud and risk, forecasting, and payment optimization. The role partners with product, engineering, architecture, and business teams to move solutions from research into production. The position develops production-ready AI/ML capabilities and scalable pipelines using modern cloud, data, and MLOps platforms. It includes end-to-end ownership of the data science lifecycle, mentoring data scientists, creating high-quality datasets, and developing generative and agentic AI applications while maintaining data integrity, privacy, security, governance, and responsible AI practices. The role is on-site Monday through Friday, with approximately 10% travel off-site or to other office locations. Candidates must currently possess valid and unrestricted U.S. work authorization; candidates requiring sponsorship now or in the future will not be considered.
Requirements
- At least 5 years of experience with a proven track record of delivering high-impact AI solutions in technology, fintech, payments, financial services, research, or consulting organizations.
- At least 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, 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.
- 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 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 payment and transaction use cases, including 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, potentially 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
