Assistant Manager - Data Analytics

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.

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:

1. 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.

2. 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.

3. 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.