Data Scientist

Role Overview: AI Specialist

• Team: Business Intelligence and AI Team.
• Focus: Enhancing data accessibility, conversational AI, and building AI-driven workflows.
• Goal: Optimize internal operations using AI solutions before expanding externally.
• Experience Level: 5+ years of overall experience.
• Leadership Quality: Some leadership qualities are expected.

Technical Requirements

• Core Skills: Experience delivering AIML production systems, conversational AI, LLMs, GPT models.
• Programming: Python and AIML libraries.
• Cloud/Platforms: Azure AI, Snowflake.
• Key Concept: Agent workflow – automating tasks by chaining AI agents (e.g., for recruitment sourcing).
• Traditional ML: Ability to build, train, and deploy custom ML models.
• Vector Database: Hands-on experience with vector databases for semantic search and efficient data retrieval in RAG implementations.
• Snowflake Cortex: Strong experience in Snowflake, specifically Snowflake Cortex for building internal UIs and applications. 1-2 years hands-on experience in Snowflake is sufficient.
• LLMs Used: Azure AI (GPT-4, GPT 4.2), exploring Gemini Pro. Traditional models include Llama-based models (e.g., 70 billion parameters).
• Data Sources: Primarily Snowflake data lake, potentially Azure and AWS S3.
• Financial Background: Good to have experience in financial or regulated industries, or an understanding of finance-related business questions.
• Testing: Basic unit testing and generation of unit test cases are required.