Sr. AI Data Scientist

Daley and Associates is currently seeking a Senior AI Data Scientist
with a leading financial services firmto develop AI solutions that improve how teams analyze information, automate workflows, and make data-driven decisions.

This is a hands-on individual contributor position with involvement across the full development lifecycle, from identifying use cases and prototyping through deployment and ongoing improvement.

This role is a hybrid position onsite 3 days per week and will pay a base salary range of $160-190K.

• Design and deploy AI and machine-learning solutions for complex business and data challenges.
• Build applications leveraging LLMs, NLP, intelligent retrieval, semantic search, and AI automation.
• Develop methods for evaluating the accuracy, reliability, and effectiveness of AI solutions.
• Partner with business and technical stakeholders to identify and implement high-value AI use cases.
• Transform complex and unstructured data into useful information, tools, and insights.
• Maintain strong standards around testing, data quality, documentation, and responsible AI.
• Evaluate new AI technologies and their potential business applications.

• 4+ years of experience in Data Science, Machine Learning, AI, Software Engineering, or a related technical field.
• 2+ years of hands-on experience with NLP, LLMs, information retrieval, or applied AI.
• Experience building and deploying AI/ML solutions beyond the prototype stage.
• Strong Python and SQL skills.
• Experience with modern AI technologies such as RAG, embeddings, vector search, knowledge-based systems, or AI agents.
• Strong understanding of machine learning fundamentals and AI evaluation.
• Experience working with complex or unstructured datasets.
• Strong communication and problem-solving skills.
• Bachelor’s or advanced degree in a quantitative or technical discipline.
• Must be authorized to work in the U.S. without sponsorship.
• Must be located in Massachusetts or willing to relocate for a hybrid schedule.

• Experience with cloud AI platforms, large-scale data processing, scientific or technical datasets, and production data pipelines is preferred.