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Mid-level ML role with popular generalist title and broad requirements increases applicant competition.
Core ML and statistical skills transferable, but insurance governance and regulated modeling add moderate domain bias.
Explicit 4–6 years plus mandatory ML, Python, and SQL skills raise candidate filtering strictness.
Build, evaluate, and deploy statistical and machine learning models (GLMs, GBMs) to meet complex business and regulatory needs.
Manage third-party data relationships including data intake, validation, and vendor engagement to ensure data quality.
Collaborate with business partners to translate analytical findings into clear, actionable recommendations and support model monitoring and governance.
4 to 6 years of relevant experience in statistical modeling and machine learning.
Bachelor's or Master's degree in Computer Science, Mathematics, Data Science, or closely related field.
Strong Python (pandas, NumPy, scikit-learn) and SQL skills; experience with Git and Unix-based development environments.
Experience with model validation, stability assessment, documentation, version control (GitHub), and experiment tracking (e.g., MLflow).
Proven expertise in GLMs and GBMs with ability to assess model performance, stability, and avoid overfitting in production-ready environments.
Experience working with multiple data sources and external vendors to ensure data quality and resolve discrepancies.
Comfortable communicating complex analytical results to both technical and non-technical stakeholders and translating insights into business recommendations.