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 driving acquisitions, cross-sell, revenue growth & improvements in client experience.

• The team extracts relevant insights, identifies business opportunities, converts business problems into analytical frameworks, uses big data tools and machine learning algorithms to build predictive models & other solutions, and designs go-to-market strategies for a huge variety of business problems.

ROLE DESCRIPTION

• The role will be Spec Analytics Analyst (C11) in the TTS Analytics team

• The role will report to the AVP/VP leading the team

• The role will involve working on multiple analyses through the year on business problems across the client life cycle – acquisition, engagement, client experience and retention – for the TTS business

• The role will involve working on multiple data science and business analytics projects throughout the year on business problems across the client life cycle – acquisition, engagement, client experience and retention – for the TTS business.

• This includes understanding business needs, designing, developing, and deploying machine learning models, and communicating insights and recommendations to stakeholders.

• The candidate should demonstrate a strong understanding of machine learning principles, model development methodologies, and deployment strategies.

• This will involve leveraging multiple 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 and machine learning solutions to business and functional stakeholders.

QUALIFICATIONS

Experience:

• Bachelor’s or Master’s Degree with 5-8 years of experience in data science and analytics

• Must have:

  • Marketing analytics experience

  • Experience on business problems around sales/marketing strategy optimization, pricing optimization, client experience, cross-sell and retention

  • Experience across different analytical methods like hypothesis testing, segmentation, time series forecasting, test vs. control comparison etc.

  • Strong hands-on knowledge of Data Science and Machine Learning, including supervised learning algorithms (both Classification and Regression) such as Linear Regression, Random Forest, XGBoost, Support Vector Machines, etc., as well as unsupervised learning techniques (e.g., clustering, dimensionality reduction).

  • Experience building and deploying time series models for forecasting and anomaly detection.

  • Experience with unstructured data analysis, e.g., call transcripts, using Natural Language Processing (NLP)/Text Mining techniques.

  • Experience building end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment.

• Good to have:

  • Experience in financial services

  • Deep learning experience; able to work with Neural Networks using TensorFlow and/or PyTorch.

  • Knowledge on working with GenAI and LLM