Data Scientist Senior Associate

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.