Astreya Internship 2026

Astreya

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6 days ago

Key Responsibilities

As a Data Science Intern at Astreya, you will work closely with experienced data engineers and cross-functional business teams to solve real-world problems through data analysis, machine learning models, and interactive visualizations.
Data Preparation & Exploration: Assist in collecting, cleaning, and preprocessing datasets from diverse sources; perform exploratory data analysis (EDA) to discover trends.
AI & Model Development: Support the development, testing, and tuning of machine learning models for tasks such as classification and predictive forecasting.
Problem-Solving: Apply analytical thinking to resolve business challenges like customer segmentation, campaign evaluation, and process optimization.
Visualization & Reporting: Create simple dashboards and reports to communicate analytical insights to stakeholders.
Collaboration & Communication: Work alongside product, business, and engineering teams to interpret project goals and present findings clearly to technical and non-technical audiences.
A/B Testing: Learn to design, execute, and analyze controlled experiments to test business hypotheses.
Data Preparation & Exploration: Assist in collecting, cleaning, and preprocessing datasets from diverse sources; perform exploratory data analysis (EDA) to discover trends.
AI & Model Development: Support the development, testing, and tuning of machine learning models for tasks such as classification and predictive forecasting.
Problem-Solving: Apply analytical thinking to resolve business challenges like customer segmentation, campaign evaluation, and process optimization.
Visualization & Reporting: Create simple dashboards and reports to communicate analytical insights to stakeholders.
Collaboration & Communication: Work alongside product, business, and engineering teams to interpret project goals and present findings clearly to technical and non-technical audiences.
A/B Testing: Learn to design, execute, and analyze controlled experiments to test business hypotheses.

Skills & Eligibility

Basic Programming: Familiarity with Python or R for data manipulation and analysis tasks.
Introductory SQL: Ability to write fundamental SQL queries to extract, filter, and aggregate relational data.
Statistical Foundations: Good understanding of basic statistics including distributions, averages, standard deviations, and correlations.
Data Science Concepts: Core knowledge of data mining, basic machine learning algorithms, and exploratory visualization tools.
Visualization Tools: Willingness to learn and explore tools like Tableau or Power BI under guided mentorship.
Strong logical reasoning and methodical problem-solving capability.
A keen eye for pattern recognition, anomalies, and data trends.
High curiosity, self-drive, and eager initiative to ask questions and learn emerging technologies.
Basic Programming: Familiarity with Python or R for data manipulation and analysis tasks.
Introductory SQL: Ability to write fundamental SQL queries to extract, filter, and aggregate relational data.
Statistical Foundations: Good understanding of basic statistics including distributions, averages, standard deviations, and correlations.
Data Science Concepts: Core knowledge of data mining, basic machine learning algorithms, and exploratory visualization tools.
Visualization Tools: Willingness to learn and explore tools like Tableau or Power BI under guided mentorship.

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