Use Python in Jupyter Notebook to process data and develop predictive models on the daily basis.
Use SQL or Python to extract the modeling data from snowflake data server.
What are some job responsibilities specific to your team for this role?
Work closely with other data scientists and actuaries in the modernization modeling team
Develop pricing and underwriting models support Auto and Property under the direct guidance of modeling leads.
Complete the model documentation and operational procedures based on USAA enterprise policy and regulatory requirements.
Attend all project update and standup meetings at the request of modeling leads and proactively provide the progress update either verbally or in written.
Hold accountable for assigned tasks with respect to quality and timeline.
Other tasks and projects assigned by modeling leads.
Please provide any details that will be helpful for Supplier to find the right candidate for job.
Quick learner and detail-oriented individual with sense of urgency, resilience to pressure, can-do attitude, and high curiosity.
Passion for data science and interest in the insurance industry to work on Auto and Property pricing and underwriting models
JOB DESCRIPTION:
Banking customer is offering an invaluable opportunity for individuals with passion for data science and interest in the insurance industry to work on Auto and Property pricing model development with the direct guidance from top talent in the industry.
Bachelor's degree is required in a STEM major (math, statistics, or data science); master's degree is strongly preferred.
It is desirable but not mandatory to have previous professional experience in building pricing loss models or underwriting models in the Property and Casualty insurance industry.
Quick learner and detail-oriented individual with sense of urgency, resilience to pressure, can-do attitude, and high curiosity.
Highly efficient in working on large-sized datasets with heterogeneous data types by using SQL and Python. The assessment for coding might be arranged during the interview.
Both conceptual understanding and working knowledge of predictive modeling are required, including but not limited to GLM, GBM, Feature Selection, Parameter Tuning, Model Diagnostics, and Performance Evaluation. A technical assessment might be arranged during the interview.
Hands-on experience in using various Python packages for the predictive modeling development, including but not limited to H2O, sklearn, statsmodels, scipy, numpy, and pandas.