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Global brand, metro location, mid-level ML analytics role with common Python/SQL skills increases candidate competition.
Core ML and analytics skills are transferable, though financial services experience is advantageous for immediate impact.
Explicit 5–9 years requirement plus mandatory ML, Python and SQL makes screening strict.
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Own end-to-end development and deployment of machine learning and predictive analytics models supporting customer retention, propensity, cross-sell, upsell, and forecasting use cases.
Translate complex business problems into actionable analytical solutions by partnering with Sales, Marketing, Distribution, Finance, and Technology teams globally.
Drive adoption of analytics outputs by effectively communicating insights and collaborating with Data Engineering and Technology teams to operationalize solutions.
5-9 years of experience in Data Science, Machine Learning or Advanced Analytics.
Strong hands-on skills in Python and SQL.
Experience building predictive models and ML solutions in commercial/business environments.
Strong understanding of statistics, regression, classification, forecasting, and model evaluation.
Experienced in customer, sales, marketing, or commercial analytics problem solving in financial services or related domains is advantageous.
Comfortable operating at the intersection of AI/ML, customer analytics, and business strategy with global stakeholder interaction.
Familiarity with cloud platforms such as AWS and Snowflake preferred for technical collaboration and model operationalization.