Understanding the Role of Senior Data Scientist in Personal Banking and Wealth Analytics
The role of a senior data scientist in personal banking and wealth analytics is a well-established professional function that requires an in-depth understanding of the banking sector and data analytics. It involves the application of disciplinary knowledge and industry expertise within a defined area with a focus on enhancing automation, decision-making, and user experience in business processes.
As an assistant vice president in this role, one has to evaluate complex issues with substantial potential impact, weigh various alternatives, and balance potentially conflicting situations using multiple sources of information. Hence, the position requires exceptional analytical skills, strong communication, and diplomacy.
Key Responsibilities of a Senior Data Scientist in Personal Banking and Wealth Analytics
The responsibilities of a senior data scientist in personal banking and wealth analytics are multifaceted and require a blend of technical and managerial skills. They must design and manage vector databases, such as FAISS and Pinecone, to support semantic search and contextual memory in AI agents. These professionals also need to conduct advanced data analysis and visualization to extract valuable insights from structured and unstructured data sources.
Moreover, staying current with emerging trends in generative AI, LLM orchestration, and agentic systems is essential to continuously improve solution design. They are also expected to develop complex data and analytical solutions using Python, PySpark, SQL to identify compliance/regulation issues, and leverage technologies like GCP Vertex AI, including Gemini models, to build scalable machine learning and generative AI applications.
Mining and Analyzing Data
In addition to the technical aspects, these professionals have to mine and analyze data from various banking platforms to derive optimization and improve data quality. They deliver analytics initiatives to address business problems, assess time and effort required, and establish a project plan.
Communicating between Business Leaders and IT
Senior data scientists must also bridge the gap between business leaders and IT, ensuring the quality of work provided by themselves and their team. They identify and communicate risks and impacts, considering the business implications of the application of technology to the current business environment.
Assessing Risk and Upholding Compliance
Finally, they are expected to assess risk when making business decisions, demonstrating particular consideration for the firm’s reputation and safeguarding the company, its clients, and assets by driving compliance with applicable laws, rules, and regulations.
Qualifications and Skills Required
For this role, you are typically required to have 5-8 years of experience in a combination of business and data analysis, process improvement, and project management. Moreover, a strong proficiency in Python, PySpark, and LangChain is essential, along with a portfolio of projects involving RAG pipelines and agentic AI.
Experience working with GCP Vertex AI, especially Gemini models, and deploying models in production environments is preferred. A solid foundation in data analytics, including statistical modeling, data wrangling, and visualization, is also required. Furthermore, strong knowledge in data engineering and database concepts is crucial.
Strong interpersonal, verbal and written communication skills, and a methodical attention to detail are equally important. In terms of education, a Bachelor’s/University degree or equivalent experience is required.
This information is derived from a job posting by Citi, which provides competitive employee benefits, including medical, dental & vision coverage, a 401(k), life, accident, and disability insurance, and wellness programs. For additional information regarding Citi employee benefits, you can visit citibenefits.com.
For more details on the role, you can visit the original job posting Here.
