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Tier-1 brand, mid-level generalist analytics role, metro location increases candidate competition.
Skills (marketing analytics, ML, NLP) are transferable, but banking domain knowledge increases specificity.
Explicit years, required ML, NLP, Python, SQL, and banking domain experience make filters strict.
Deliver analytical insights to support sales, marketing strategy optimization, pricing, client experience, cross-sell, and retention in Treasury & Trade Services business.
Leverage multiple data sources including client profiles, transaction data, digital and unstructured data (e.g., call transcripts) using predictive modeling and machine learning techniques.
Convert complex business problems into analytical frameworks and present actionable insights to business and functional stakeholders.
Bachelor’s degree with 5-8 years or Master’s degree with 4-8 years in data analytics or PhD-level experience.
Proven experience in marketing analytics related to sales/marketing strategy optimization, pricing optimization, client experience, cross-sell, and retention.
Proficiency in Python/R, SQL, and experience with big data tools such as Hive; predictive modeling using machine learning and unstructured data analysis using NLP/Text Mining.
Not explicitly mentioned: Notice period, mandatory location, or specific regulatory requirements.
Strong ability to translate complex business problems into analytical solutions across various data types and domains within sales and marketing.
Experience working with large and diverse data sets, combining structured and unstructured data, and delivering insights through advanced analytical methods like hypothesis testing, segmentation, forecasting, and test vs control.
Demonstrated ability to communicate findings effectively to management and mentor junior team members, with skills in project management and organizational contribution.