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
