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Data Science Expert
2 weeks ago
LexisNexis Risk Solutions is a leading provider of risk assessment solutions. Our Insurance vertical offers cutting-edge tools and decision-making support that combines public and industry-specific content with advanced technology and analytics.
We help drive better data-driven decisions across the insurance policy lifecycle, reducing risk while enhancing operational efficiency. Our team provides opportunities to engage with the entire insurance industry, including large, medium, and small companies, allowing for a comprehensive understanding of the sector.
We regularly exchange and learn from our 130+ Data Science teams globally, expanding our international perspective. As part of this team, you will be working on evolving our infrastructure capabilities and supporting the Analytics Data Science cloud and Product migration.
Key responsibilities include data value assessment, data mining, and analytical modelling for product validation and implementation. You will leverage strong data handling skills to do complex data cleaning and processing tasks, providing essential modelling support to the modelling needs of the analytics team.
Collaborate closely with the product team to design model application solutions, creating industry-leading analytical products. Stay informed about the latest developments in the insurance industry, actuarial field, and data science, providing updates on new technologies and industry insights.
Utilize strong Data and Computer Science knowledge working closely with their Data Science team, product, sales, and other data teams. Support diverse external and internal customer test projects, including product tests, implementation, and client communication, ensuring smooth project delivery.
This role requires a Bachelor's degree or higher, with significant experience in Data Science. Preferable knowledge of actuarial pricing modelling in the insurance market, actuarial department, or regional actuarial field. Proficiency in Python or R, with experience in modeling and project work using GLMs (Generalized Linear Models), GBDT/XGBoost/LightGBM/CatBoost, or other machine learning algorithms.
Advanced data processing skills with a deep understanding of and experience in applying data science libraries such as numpy, pandas, and sklearn. Design, build, and maintain pipelines for large-scale data processing using Azure services like Azure Databricks, Azure Data Factory, and Azure ML studio.
