Treasury And Trade Services Analytics Citi

About Citi

Job Description

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 focuses on driving acquisitions, cross-sell, revenue growth, and improvements in client experience. They extract relevant insights, identify business opportunities, convert business problems into analytical frameworks, use big data tools and machine learning algorithms to build predictive models and other solutions, and design go-to-market strategies for a variety of business problems. The role of Spec Analytics Analyst 2 (C10) in the TTS Analytics team involves working on multiple analyses throughout the year on business problems across the client life cycle - acquisition, engagement, client experience, and retention for the TTS business. This includes leveraging various analytical approaches, tools, and techniques, working on multiple data sources (client profile & engagement data, transactions & revenue data, digital data, unstructured data like call transcripts, etc.) to provide data-driven insights to business and functional stakeholders. Qualifications: Experience: - Bachelor's Degree with 3+ years of experience in data analytics or Master's Degree with 2+ years of experience in data analytics. Must have: - Identifying and resolving business problems in areas such as 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 Predictive Modeling techniques for a wide range of business problems. - Working with data from different sources, with different complexities, both structured and unstructured. - Utilizing text data to derive business value by leveraging different NLP techniques. Good to have: - Experience working with data from different sources and of different complexity. Skills: Analytical Skills: - Proficient in formulating analytical methodology, identifying trends and patterns with data. - Ability to work hands-on to retrieve and manipulate data from big data environments. Tools and Platforms: - Proficient in Python/R, PySpark, and related tools. - Experience in Hive. - Proficient in MS Excel, PowerPoint. Good to have: - Experience with PySpark. - Experience with Tableau. Soft Skills: - Strong analytical and problem-solving skills. - Excellent communication and interpersonal skills. - Organized, detail-oriented, and adaptive to a matrix work environment.,