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Strong employer brand, generalist data-science role, metro location, and mid-level experience drive high competition.
Requires credit risk and credit-bureau domain knowledge, limiting cross-industry transferability moderately.
Specific technical stack and domain experience required but low years threshold, so filters are moderately strict.
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Design and develop predictive and audience models using proprietary data to support client acquisition and engagement strategies.
Build and execute descriptive, predictive, and prescriptive analytics use cases employing regression, clustering, segmentation, and ensemble modeling techniques.
Manage end-to-end analytics lifecycle including model development, deployment support, performance monitoring, and generating actionable business insights from large-scale transactional and demographic data.
2+ years of analytics experience (including internships) in financial services or related industries.
Bachelor’s degree in a quantitative field such as Statistics, Mathematics, Engineering, or Operations Research, or equivalent experience.
Experience with statistical and machine learning techniques like regression, clustering, and ensemble models.
Experience working with large-scale data environments and proficiency in Python, R, SQL, and Spark for data extraction, transformation, and analysis.
Familiarity with financial services domain, specifically credit risk and marketing analytics, to interpret consumer data effectively.
Ability to collaborate with internal stakeholders and external clients in a matrixed, global environment to deliver analytics solutions with measurable outcomes.
Experience operating with moderate supervision in complex environments, demonstrating independent delivery and innovation in analytics solutions.