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Tier-1 employer, metro location, attractive mid-level ML role with broad skillset increases applicant competition.
Core ML and data engineering skills transfer across industries, but payments/product deployment context raises specificity.
No explicit years but specific ML, data engineering, and deployment tech required, so screening will be moderately strict.
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Design, develop, and maintain advanced analytics and machine learning solutions to optimize digital payment products and drive business impact globally.
Lead end-to-end development, validation, and monitoring of predictive models ensuring scalability, performance, and alignment with business objectives.
Translate complex data science results into actionable insights for senior stakeholders and collaborate cross-functionally to implement data-driven optimizations.
Experience Required: Not explicitly mentioned in the JD; relevant industry experience implied by responsibilities.
Proficiency in Python, Spark, Hadoop platforms (Hive, Impala, Airflow, NiFi), and SQL.
Strong academic background or equivalent experience in Computer Science, Data Science, Technology, Mathematics, Statistics, or related field.
Experience with building and deploying machine learning models and handling large-scale data pipelines.
Experienced in developing scalable, production-level machine learning and analytics solutions in fast-paced, deadline-driven environments.
Able to work cross-functionally with global teams and translate technical findings into business insights and sales enablement.
Knowledgeable about emerging AI and data science technologies with a focus on responsible, compliant, and resilient AI frameworks.