Data Scientist

Description :

Location Address : 44 King Street W 23rd Floor

Work arrangement : ( Hybrid - In office 2-3 days per week)

Contract Duration : March 2nd till Oct 31st

Possibility of extension : Depending on performance

Conversion to FTE Depending on performance

Schedule Hours : 9 am-5 pm Monday-Friday (No overtime).

Story Behind the Need

Business group : Global AI & ML - specializing in advanced applications of AI models, Retrieval Augmented Generation (RAG) architectures, and Agentic AI.

Project / Initiative : Mainly working on the real-time agent assistance design / implementation of features.

You'll play a pivotal role in designing and implementing next-generation AI systems, collaborating with experts from various fields, including data engineers, software developers, and business analysts.

Candidate Value Proposition :

You'll be part of our dynamic team at the intersection of various business groups and cutting-edge generative artificial intelligence.

This role offers you a unique opportunity to make a real-world impact by enhancing customer experiences and operational efficiency through innovative LLM-driven and agentic AI solutions applied across data-rich banking operations.

Typical Day in Role :

• Determine the "right tool for the job"—balancing the precision of deterministic AI with the flexibility of RAG and AI models.
• Gather and clean relevant data sets for training and fine-tuning Large Language Models (LLMs), and building Retrieval Augmented Generation (RAG) systems.
• Ensure data quality, consistency, and compliance with regulatory requirements.
• Develop and fine-tune specialized Large Language Models (LLMs) and advanced Natural Language Understanding (NLU) techniques for diverse banking use cases, including complex information extraction, multi-turn dialogue management, and financial text summarization.
• Design, develop, and optimize intelligent, agentic conversational AI systems (e.G., advanced virtual assistants, automated financial advisors) leveraging LLMs for seamless customer interactions across various channels, including text and voice.
• Implement and refine advanced Natural Language Understanding (NLU) and Natural Language Generation (NLG) techniques, particularly within Retrieval Augmented Generation (RAG) architectures, to enhance the accuracy, relevance, and explainability of AI responses by grounding them in authoritative data sources.
• Assist in the strategic implementation and deployment of Large Language Models (LLMs) for a wide array of banking applications beyond just customer interfaces, such as automated content generation (e.G., reports, summaries of financial documents), risk assessment insights, and internal knowledge management.
• Analyze interactions with LLM-powered agents and RAG systems to identify emergent patterns, user needs, and critical areas for performance optimization and continuous improvement.
• Generate actionable insights derived from LLM and Agentic AI analysis to strategically inform business decisions and drive innovation.
• Evaluate model performance using relevant metrics and develop strategies for model optimization and bias mitigation.
• Continuously monitor, update, and re-train models to adapt to changing customer behavior, market dynamics, and evolving data landscapes.
• Create dashboards and reporting for a diverse range of stakeholders.
• Ensure strict adherence to data privacy and security standards, especially when handling sensitive customer data within generative AI systems.
• Lead the operationalization of Hybrid Architectures where traditional Intent-based bots meet Generative AI.

Documentation and Reporting :

Collaboration and Communication :

Candidate Requirements / Must Have Skills :

Nice-To-Have Skills :

Best VS. Average Candidate :