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Tier-1 brand, metro locations, and broad generalist ML/analytics skillset increase candidate competition.
Requires core ML/data skills but fintech fraud domain knowledge increases industry-specific fit sensitivity.
Multiple mandatory technical skills and an explicit experience band make screening moderately strict.
Develop, deploy, and validate predictive models to enable profitable decisions across risk, fraud, and marketing domains.
Analyze large datasets to generate business insights and create innovative machine learning solutions using big data technologies.
Communicate business findings clearly to leadership and partners; stay updated on developments in finance, payments, and analytics fields.
Master's degree in MBA, Economics, Statistics, Computer Science, or related fields.
0-30 months of experience in analytics or big data workstreams.
Technical skills in SAS, R, Python, Hive, Spark, SQL, and familiarity with supervised and unsupervised machine learning techniques.
Work Experience Required: 0-30 months in analytics or big data; Notice period: Not explicitly mentioned in the JD.
Comfortable working with complex, unstructured data and applying advanced machine learning techniques including reinforcement learning and Bayesian models.
Capable of integrating and collaborating effectively with cross-functional global business partners.
Able to drive project deliverables independently and communicate findings to senior leadership clearly.