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Mid-level ML role, broad skills, and known global employer create moderate candidate competition.
Core ML, Python, SQL, and cloud skills are transferable, though credit-risk domain experience provides advantage.
Explicit 5–7 years requirement plus mandatory ML, Python, SQL, and cloud/MLOps skills increase filtering strictness.
Design, build, and optimize predictive models and machine learning pipelines to support business strategy and customer decision points across the consumer lending lifecycle.
Extract, clean, and feature-engineer large-scale structured and unstructured datasets to develop robust, reusable, and automated analytical solutions.
Present technical findings and model performance to mid-level leadership and external stakeholders, and conduct code and model reviews to maintain governance standards.
Bachelor's or Master's degree in STEM (Data Science, Statistics, Computer Science, or related quantitative discipline).
5–7 years of hands-on experience in predictive modeling, statistical analysis, and machine learning deployment in real applications.
Advanced proficiency in Python and SQL; experience with enterprise cloud platforms (GCP, AWS, or Azure).
Work Experience Required: 5–7 years in relevant data science roles.
Experienced in financial services data science domains such as credit risk, marketing analytics, or collections.
Skilled in handling large-scale datasets using PySpark, BigQuery, Snowflake, or Hadoop platforms.
Familiarity with Google Cloud Platform certifications and MLOps best practices including Git, CI/CD pipelines, and model governance.