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Tier-1 brand, mid-level generalist data role with broad stack and metro hiring draws high competition.
Core data science skills transfer well, but credit/fraud domain knowledge raises industry specificity.
Explicit 2–4 year requirement plus mandatory tech stack and domain expertise increases shortlisting strictness.
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Develop, review, and execute economic logic and analytical solutions to drive profitable credit business actions.
Leverage supervised and unsupervised machine learning techniques including neural models, decision trees, reinforcement learning, and others for risk management and fraud detection.
Integrate and collaborate with cross-functional global business partners to implement data-driven solutions impacting millions of customers.
MBA or master's degree in Economics, Statistics, Computer Science, or related fields.
24-48 months of experience in analytics and big data workstreams.
Proficiency in SAS, R, Python, Hive, Spark, and SQL.
Work Experience Required: 2 to 4 years in analytics or data science roles.
Experience with advanced coding, algorithms, and high-performance computing techniques.
Ability to drive project deliverables to achieve measurable business results in credit risk or fraud domains.
Comfortable working independently with complex, unstructured initiatives and collaborating with global cross-functional teams.