Data Scientist, Specialist

Responsibilities:

Solve Business Problems with AI

• Partner with business stakeholders to identify, frame, and prioritize high-value problems that can be addressed using Agentic AI, LLMs, and NLP.
• Translate business needs into clear AI use cases, success metrics, and delivery plans.
• Focus on business outcomes, not just model performance.

• Architect and develop agentic AI systems that can reason, plan, and take actions across tools, workflows, and data sources.
• Design multi-agent and tool-augmented LLM solutions to automate complex, multi-step processes.
• Ensure solutions are reliable, explainable, and governed for enterprise use.

• Collaborate with engineering teams to deploy AI solutions with scalability, security, and performance in mind.
• Implement evaluation, monitoring, and guardrails for LLM and agentic systems, including bias, drift, and failure modes.
• Align solutions with enterprise risk management, compliance, and responsible AI standards.

• Act as a trusted AI advisor, helping teams understand where Agentic AI and LLMs add value-and where they do not.
• Contribute to AI best practices, reusable patterns, and strategic direction.
• Mentor peers and teammates on applied AI and business-driven problem solving.

• Agentic AI: Experience designing AI agents that reason, plan, and act across systems.
• Large Language Models (LLMs): Hands-on experience building enterprise LLM applications (e.g., RAG, tool use, orchestration, evaluation).
• Natural Language Processing (NLP): Strong experience working with unstructured text and language-driven workflows.

• MS or PhD in Computer Science, Machine Learning, Data Science, or a related quantitative field.
• 3+ years delivering AI/ML solutions in production environments.
• 5+ years of hands-on Python experience; experience with distributed data processing is a plus.
• Strong ability to solve business problems using AI, not just build models.
• Excellent communication skills, with the ability to explain complex concepts to both technical and non-technical audiences.
• Experience working in cross-functional, enterprise environments.