Data Analyst Intern (6 months) - RPD

Key Responsibilities

• Data Analysis & Insights:
• Analyze large datasets related to insurance premiums, customer behavior, revenue trends, and monetization performance.
• Identify patterns, trends, and anomalies to provide actionable insights.
• Data Cleaning & Preparation:
• Process, clean, and validate data from various sources to ensure accuracy and reliability.
• Tool Development Support
• Collaborate with the product and development teams to integrate data insights into new tools.
• Assist in the automation of data processes for scalability and efficiency.
• Basic Machine Learning Models:
• Assist in building and deploying basic machine learning models, such as regression, classification, and clustering models, to uncover predictive insights.
• Use ML techniques to identify trends and predict outcomes,.
• Revenue Optimization:
• Work with the team to identify opportunities to optimize site monetization strategies through ad placements and targeting.
• Assess ad performance metrics like click-through rates (CTR), cost per click (CPC), and impressions to propose data-driven strategies.
• Dashboard Creation & Reporting:
• Design and maintain dashboards to track KPIs for revenue and insurance metrics.
• Present findings through reports, charts, and visualizations to key stakeholders.
• Collaboration:
• Partner with cross-functional teams to understand data needs and ensure alignment of insights with business goals.
• Support ad hoc analytical requests from stakeholders.

Qualifications & Skills

Required:

• Currently pursuing or recently completed a degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering or a related field.
• Proficiency in data analysis tools such as Python, R, or SQL.
• Familiarity with data visualization tools like Tableau, or Power BI, and Excel.
• Basic understanding of statistical concepts and techniques.
• Exposure to machine learning frameworks such as Scikit-learn or TensorFlow.
• Strong analytical and problem-solving skills with attention to detail.
• Excellent written and verbal communication skills.

Preferred:         

• Experience applying basic ML models (e.g., linear/logistic regression, decision trees, or k-means clustering) in real-world scenarios.
• Exposure to large datasets, especially in insurance, finance, or ad tech domains.
• Knowledge of web analytics tools (e.g., Google Analytics).
• Familiarity with predictive modeling techniques and pipeline building.
• Experience working with ad monetization platforms (e.g., Google AdSense, programmatic ads).