Invesco
Data Scientist
Overview
We are seeking a Data Scientist in our Hyderabad office who treats engineering as a core discipline and AI as their primary creative partner to build and validate living agentic systems within our business workflows.
About Invesco
As one of the world’s leading independent global investment firms, Invesco is dedicated to rethinking possibilities for our clients. By delivering the combined power of our distinctive investment management capabilities, we provide a wide range of investment strategies and vehicles to our clients around the world.
Requirements & Eligibility
- 3-5 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 and CI/CD.
- 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.
Key Responsibilities
- Collaborating 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.
- Building and maintaining data pipelines and validation layers that feed our AI tools, ensuring high-quality structured and alternate data inputs.
- 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.
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