Data Scientist

The Spec Analytics Analyst 2 is a developing professional role. Applies specialty area knowledge in monitoring, assessing, analyzing and/or evaluating processes and data. Identifies policy gaps and formulates policies. Interprets data and makes recommendations. Research and interprets factual information. Identifies inconsistencies in data or results, defines business issues and formulates recommendations on policies, procedures or practices. Integrates established disciplinary knowledge within own specialty area with basic understanding of related industry practices. Good understanding of how the team interacts with others in accomplishing the objectives of the area. Develops working knowledge of industry practices and standards. Limited but direct impact on the business through the quality of the tasks/services provided. Impact of the job holder is restricted to own team. What do we do? The TTS Analytics team provides analytical insights to the Product, Pricing, Client Experience and Sales functions within the global Treasury & Trade Services business. The team works on business problems focused on enhancing client experience, driving acquisitions, cross-sell and revenue growth. The team extracts relevant insights, identifies business opportunities, converts business problems into analytical frameworks, uses big data tools and AI/ML techniques to drive data driven business outcomes in collaboration with business and product partners. The team works on building and operating Generative AI and deep learning solutions - Design, implement, and scale production‑grade AI applications end‑to‑end—from data ingestion and model services to user‑facing interfaces, observability, and secure deployments.

DESCRIPTION The role will be Spec Analytics Analyst 2 (C10) in the TTS Analytics team The role will report to the AVP or VP leading the team The role will involve working on Multiple analyses through the year on business problems across the client experience for the TTS business This will involve leveraging multiple analytical approaches, tools and techniques, working on multiple data sources (unstructured data like emails, call transcripts, etc., client profile & engagement data, transactions & revenue data, digital data, etc.) to provide data driven insights to business partners and functional stakeholders Translating business data into structured, usable formats and determine the appropriate ingestion approach (Graph vs Vector RAG) based on use case and data characteristics. Working on Gen AI projects involving data analysis, prompt tuning, RAG, Agentic RAG, etc. Collaborate with product, business, operations and data teams to prioritize and translate ambiguous problems into delivered capabilities.

Experience: Bachelor’s Degree with 3+ years of experience in analytics or master’s degree with 2+ years of experience in analytics Must have: Identifying and resolving business problems (around sales/marketing strategy optimization, pricing optimization, client experience, cross-sell and retention) preferably in the financial services industry Leveraging and developing analytical tools and methods to identify patterns, trends and outliers in data Applying ML, DL and Gen AI techniques like RAG, prompt Engineering, fine tuning for a wide range of business problems Working with data from different sources, with different complexities, both structured and unstructured Collaborate with product, business, operations and data teams to prioritize and translate ambiguous problems into delivered capabilities.

Analytical Skills: Proficient in formulating analytical methodology, identifying trends and patterns with data Has the ability to work hands-on to retrieve and manipulate structured and unstructured data from big data environments Gen AI Development experience in RAG, Prompt Tuning, Agentic RAG, etc. Tools and Platforms: Proficient in Python/R, PySpark, Data crunching, Prompt Tuning, RAG Good to