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High - Tier-1 brand, metro location, mid-level generalist ML/Data role, and common toolset increase applicant density.
Medium - core ML skills transfer across industries, but CX and credit-card domain experience increases fit.
High - explicit years requirement plus mandatory Python, SQL and ML plus production deployment skills.
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Analyze and interpret large, complex customer experience data sets to identify trends, pain points, and opportunities for improving North America Consumer Bank’s customer satisfaction.
Develop, document, and deploy data-driven business strategies using tools like SQL, Python, and machine learning models with responsibility for code quality, testing, deployment, and model validation in production.
Collaborate with multiple stakeholders and lead small/global teams to implement customer experience improvements, including risk assessment and adherence to compliance policies.
Bachelor’s degree with at least 5 years of work experience OR Master’s degree with 4 years of work experience in quantitative fields (Economics, Statistics, Mathematics, IT, Computer Applications, Engineering, etc.).
Strong hands-on experience in Python, SQL, and Machine Learning is mandatory.
Experience working with large data sets and familiarity with data processing, cleaning, and visualization; knowledge of version control (Git) and automated build processes (Jenkins).
Work Experience Required: Minimum 4-5 years as mentioned; experience with customer experience analytics or credit card business is preferred but not mandatory.
Experienced in applying machine learning and data science techniques specifically in customer experience analytics within financial services, ideally with exposure to credit card business.
Comfortable working in Agile environments with SDLC knowledge and experience leading or mentoring small to global teams.
Strong technical proficiency in developing, testing, deploying, and troubleshooting production code pipelines, with attention to compliance and risk management in financial domain.