Principal Data Scientist

Responsibilities

• Leading and collaborating with software engineers, data engineers, and investment professionals to build innovative AI-based tools using structured and alternate data sources.
• Designing and implementing complex reasoning loops using frameworks like LangGraph, LangChain, or AgentCore, transforming static LLMs into dynamic, goal-oriented agents.
• Developing robust evaluation frameworks and "gold-standard" datasets to measure the performance, faithfulness, and accuracy of generative outputs.
• Scaling AI initiatives and agentic development across Invesco Technology teams by creating reusable patterns, prompt templates, agentic harnesses, skills, and subagents with GitHub Copilot and Claude Code.
• Collaborating with full stack engineers to integrate AI models into business workflows, using AI-assisted coding to move from a notebook experiment to a functional microservice in record time.
• Implementing systematic testing (e.g., RAG evaluation, stress testing) to ensure AI solutions meet the high compliance and reliability standards of the financial services industry.
• Applying emerging techniques in prompt engineering, fine-tuning, and multi-agent systems to solve unique business challenges.

Requirements

• 7-10 years of demonstrated experience in data science or machine learning, with a strong emphasis on Natural Language Processing (NLP) and Generative AI.
• Strong foundation in Python and cloud-native development; you write clean, modular code and are comfortable with Docker, CI/CD, and DevSecOps.
• Exposure to or high enthusiasm for learning LangGraph, LangChain, Copilot Studio, or AgentCore with Strands.
• Familiarity with creating evaluation metrics and building datasets for model validation and testing.
• Proactive use of AI tools (GitHub Copilot, Cursor, etc.) to accelerate technical tasks and explore new libraries.
• A desire to work at the intersection of data engineering and full-stack development, with the agility to pivot based on rapid sprint feedback.

Core Competencies

Demonstrates expertise in Natural Language Processing (NLP) and Generative AI, with a strong foundation in Python and cloud-native development. Capable of leading AI initiatives, implementing evaluation frameworks, and collaborating effectively across teams to integrate AI solutions into business workflows.