We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Data Scientist Senior Associate within our Business Banking Data and Analytics Team, you will be instrumental in constructing predictive models and developing robust RAG pipelines. Collaborating closely with cross-functional teams, you will extract valuable insights from complex datasets to promote data-driven decision-making across the organization. Your focus will include addressing problem statements and innovating solutions for complex challenges. Additionally, you will build AI-based solutions to enhance both technological and business efficiency.

Job responsibilities

Align ML problem definition with business objectives to ensure solutions address real-world needs.
Design, develop, and manage prompt-based models on Large Language Models (LLMs) for complex financial services tasks.
Architect and oversee the development of next-generation machine learning models and systems using cutting-edge technologies.
Drive innovation in machine learning solutions, focusing on scalability, flexibility, and future-proofing.
Promote software and model quality, integrity, and security throughout the organization.
Architect and implement scalable AI Agents, Agentic Workflows, and GenAI applications for enterprise deployment.
Integrate GenAI solutions with enterprise platforms using API-based methods.
Establish validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails.
Collaborate with technology teams to lead the design and delivery of GenAI products.

Required Qualifications, Capabilities, and Skills

Formal training or certification in software engineering concepts and 8+ years of applied AI/ML experience.
Strong understanding of the Software Development Life Cycle (SDLC), Data Structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, and Statistics.
Experience with cloud platforms such as AWS, GCP, or Azure.
Proficiency in RDBMS, NoSQL databases, and prompt design.
Demonstrated expertise in machine learning frameworks such as TensorFlow, PyTorch, pyG, Keras, MXNet, and Scikit-Learn.
Proficient in building AI Agents (e.g., LangChain, LangGraph, AutoGen), integration of tools (e.g., API), and RAG-based solutions (e.g., open search), Knowledge Graphs(e.g., neo4J).
Proven track record of building and scaling software and/or machine learning platforms in high-growth or enterprise environments.
Exceptional ability to communicate complex technical concepts to both technical and non-technical audiences.