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Mid-level popular Data Scientist role with broad, common ML and analytics skills, increasing applicant density.
Financial services model risk, credit operations, and regulatory knowledge reduce cross-industry transferability.
Explicit 2–5 years, required ML/tool skills, and regulated financial model governance raise filtering strictness.
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Apply predictive modeling and data mining techniques in big data environments to deliver models and actionable business insights.
Ensure models comply with regulatory standards and measure financial impact to the business.
Support implementation, monitoring, and improvement of analytical solutions while collaborating with internal teams and stakeholders.
Bachelor’s degree in Statistics, Mathematics, Engineering, Data Science, Economics, Computer Science, or a related quantitative field.
2 to 5 years of related work experience in statistical or machine learning modeling and data mining.
Experience in extracting, processing large datasets and interpreting model results for business recommendations.
Work location and notice period: Not explicitly mentioned in the JD.
Experience in financial services domain with foundational understanding of credit card operations and relevant regulations.
Proficiency with programming and analytical tools such as Python, R, SAS, SQL, Spark SQL, and data visualization (PowerPoint, pivot tables).
Demonstrates ability to work under supervision to translate analytical findings into recommendations and collaborate across data science teams.