Internship - Data Analyst
About Us
Liquidnitro Games is India’s flagship live services and game production company, founded by industry veterans with a proven track record in producing massively successful games & live services. For game companies, studios and publishers worldwide – we offer world class game development expertise to power creativity, growth and profitability in their games.
What’s in it?
The Data Analyst Intern will work closely with Product, Analytics, Engineering, and other cross-functional teams to use data to understand player behavior, product performance, and business outcomes.
The role is focused on building strong analytical fundamentals through hands-on experience with real product data. The intern will support exploratory analysis, reporting, dash boarding, experimentation, and adhoc investigations, while learning how to translate business and product questions into structured analytical problems.
The ideal candidate is curious about data, enjoys solving problems, and is eager to understand not just what happened, but also why it happened and what can be learned from it.
Responsibilities
- Support analysts and product teams with data analysis and exploratory data analysis to identify trends, patterns, and opportunities.
- Write SQL queries to extract, transform, and analyze data from large datasets.
- Analyze player behavior, engagement, retention, monetization, and gameplay patterns to support product decisions.
- Build and maintain reports and dashboards using tools such as Tableau, Power BI, or similar BI platforms.
- Assist with A/B test and experiment analysis, including metric calculation, cohort analysis, and interpretation of results.
- Perform data validation and quality checks to ensure analysis is accurate and reliable.
- Support player segmentation and clustering exercises based on behavioral and engagement patterns.
- Investigate business and product questions through structured analysis and communicate key findings to stakeholders.
- Work with Product Managers, Engineers, and other analysts to understand requirements and translate them into analytical tasks.
- Assist in defining and validating game telemetry and event data required for product analysis.
- Document analysis, metrics, assumptions, and methodologies so that work can be reproduced and reused.
- Learn and apply statistical and analytical techniques to solve real-world product problems.
- Present findings clearly through charts, dashboards, and concise written or verbal recommendations.
Skill Requirements
- Currently pursuing or recently completed a degree in Computer Science, Statistics, Mathematics, Economics, Engineering, Data Science, or a related field.
- Strong interest in data analytics, product analytics, gaming, or consumer products.
- Basic to intermediate SQL knowledge and familiarity with querying structured datasets.
- Basic understanding of statistics, including averages, distributions, correlation, sampling, and hypothesis testing.
- Familiarity with Excel / Google Sheets for data analysis.
- Basic knowledge of Python or R is preferred.
- Familiarity with data visualization tools such as Tableau, Power BI, or Looker is a plus.
- Good problem-solving and analytical thinking skills.
- Ability to communicate findings clearly and explain analytical conclusions using data.
- Strong attention to detail and willingness to validate assumptions and results.
- Curiosity and eagerness to learn new analytical techniques, tools, and technologies.
What You Will Learn
- How product and business teams use data to make decisions.
- How to translate product questions into structured analytical problems.
- How to work with real-world, large-scale datasets using SQL.
- Product analytics concepts such as DAU, retention, engagement, monetization, conversion, and player segmentation.
- Fundamentals of A/B testing and experimentation.
- Exploratory data analysis and statistical reasoning.
- Data visualization and analytical storytelling.
- How game telemetry and event data are designed and used for analytics.
- How to collaborate with Product, Engineering, and Analytics teams.
- How to communicate insights and recommendations to stakeholders.