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Tier-1 employer, mid-level ML role, metro location and generalist skillset drive high competition.
Core ML skills transfer across industries, but GLM pricing and insurance domain preference increase specificity.
Explicit 4–6 years requirement plus mandatory ML stack, Azure/Databricks and domain preferences increase strictness.
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Develop and deploy machine learning and generalized linear models to support pricing strategies for property and casualty insurance products.
Collaborate with global business partners to manage project deliverables, gather requirements, and present analytical insights to technical and non-technical stakeholders.
Build, validate, and maintain predictive models and reusable modeling frameworks, leveraging statistical analysis and data preprocessing techniques.
4-6 years of work experience in building models.
Master’s degree in Statistics, Mathematics, Analytics, Computer Science, Engineering, Data Science, Business Analytics, or passed actuarial exams.
Proficiency in Python/PySpark, including OOP, design patterns, packaging/environment management, and ML libraries such as scikit-learn and H2O.
Strong knowledge of machine learning concepts including GLM, regression, ensemble techniques (Random Forest, XGBoost, SVM), clustering, probabilistic models, and experience working with big data technologies and Azure/Databricks platforms.
Experienced in applying advanced statistical and machine learning techniques to complex business problems, especially in insurance or actuarial contexts.
Skilled at managing stakeholder communications across technical and non-technical audiences in a cross-functional global environment.
Comfortable handling end-to-end model lifecycle including research, development, deployment, validation, and maintenance using modern analytics tools and cloud-based data pipelines.