Develop and implement advanced data analytics and predictive modeling techniques to analyze complex datasets and to support business objectives
Manage and analyze large datasets to identify patterns, correlations, and trends.
Perform advanced analytics to extract actionable insights from large, structured, and unstructured data sets
Design and maintain predictive models for risk assessment, pricing, and customer segmentation.
Utilize data visualization tools and techniques to present findings and insights to stakeholders in a clear and compelling manner.
Stay informed of industry trends and advancements in data analytics and predictive modeling.
Contribute to the improvement of data quality and reliability by identifying data inconsistencies and gaps.
Create and maintain robust data pipelines for efficient data extraction, transformation, and loading (ETL) processes.
Ensure adherence to regulatory standards and company policies, as well as stay abreast of industry trends, technologies, and best practices in data science and health insurance.
Collaborate with cross-functional teams to understand business requirements and deliver comprehensive analytics solutions.
Skills
Strong academic background or relevant project/internship experience in data science or analytics.
Proficiency in programming languages such as Python and R.
Strong understanding of SQL and experience with database technologies.
Familiarity with statistical software such as SAS.
Experience with data visualization tools, preferably Power BI.
Knowledge of data engineering principles and practices is a plus.
A foundational understanding of statistical modelling, machine learning algorithms, and data analysis techniques.
