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High: Tier-1 bank, mid-level generalist analytics title, and metro location increase applicant density.
Medium: analytics and ML skills transfer across industries, but finance/marketing domain experience is preferred.
High: explicit 5–8 years requirement plus mandatory ML/NLP, marketing analytics, and tooling skills.
Work on multiple analytical projects throughout the year addressing business problems across client life cycle stages such as acquisition, engagement, experience, and retention for the Treasury & Trade Services (TTS) business.
Leverage multiple analytical methods and tools including predictive modeling with machine learning and analysis of structured and unstructured data sources (e.g., transactions, client data, call transcripts) to deliver data-driven insights to business and functional stakeholders.
Convert complex business problems into analytical frameworks and design go-to-market strategies to drive acquisitions, cross-sell, revenue growth, and client experience improvements.
Bachelor's degree with 5-8 years, or Master's degree with 4-8 years of experience in data analytics, or PhD.
Mandatory experience in marketing analytics focused on sales/marketing strategy optimization, pricing optimization, client experience, cross-sell, and retention.
Proficiency in Python or R, SQL, and data querying platforms like Hive; experience with machine learning predictive modeling and unstructured data analysis using NLP/Text Mining.
Work Experience Required: 4-8 years (depending on degree) in data analytics; specific marketing analytics experience mandatory.
Experienced analyst with strong skills in applying diverse analytical methods such as hypothesis testing, segmentation, time series forecasting, and test vs. control comparisons in marketing and client-related business contexts.
Capable of hands-on data retrieval and manipulation in big data environments, demonstrating proficiency in Python/R, SQL, and working with complex multi-source data, including unstructured datasets.
Strong communicator able to translate analytical findings into clear business insights; experienced in mentoring juniors and managing projects within analytics teams focused on financial services or related sectors.