About the Job:
The role of Assistant Manager - Data Analytics involves supporting data-driven decision-making processes and developing data & AI solutions. This includes analyzing data to predict risks, assess scenarios, and provide actionable insights that aid informed decision-making.
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Role Purpose:
To support with data-driven decision-making processes and build data & AI solutions by analyzing data to predict, assess risks, and provide actionable insights to support informed decision-making.
Key Accountabilities:
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Data Analysis and Predictive Modeling
- Implement advanced data analytics and predictive modeling techniques to analyze complex datasets to support business objectives.
- Analyze large datasets to identify patterns, correlations, and trends.
- Perform advanced analytics to extract actionable insights from large data sets.
- Maintain predictive models for risk assessment, pricing, and customer segmentation.
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Reporting and Communication
- Utilize data visualization tools and techniques to present findings and insights to stakeholders in a clear and compelling manner.
- Research updates on industry trends and technological advancements in data analytics and predictive modeling.
- Collaborate with cross-functional teams to understand business requirements and deliver comprehensive analytical solutions.
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Innovation & Compliance
- Identify data inconsistencies to contribute to the improvement of data quality and reliability.
- Create robust data pipelines for efficient data extraction, transformation, and loading (ETL) processes.
- Ensure adherence to regulatory standards and company policies for best practices in data science and health insurance.
Skills Required:
- Strong background or relevant project/internship experience in data science or analytics.
- Proficiency in statistical analysis and familiarity with AI development.
- Proficient 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.
- Foundational understanding of statistical modeling, machine learning algorithms, and data analysis techniques.
Education:
- Degree in Statistics, Mathematics, Actuarial Science, Finance, Management Information Systems (MIS), or Software Engineering.
