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Data Science Intern - Remote

Posted 2 days ago
Data Analysis
Internship
India

Overview

As a Data Science Intern, you will collaborate with our data science and analytics teams to work on meaningful projects involving data analysis, predictive modeling, and statistical modeling.

In Short

  • Assist in collecting, cleaning, and preprocessing data from various sources.
  • Perform exploratory data analysis to identify trends, patterns, and anomalies.
  • Develop and implement machine learning models and algorithms.
  • Create data visualizations and reports to communicate findings to stakeholders.
  • Collaborate with team members on data-driven projects and research.
  • Participate in meetings and contribute to discussions on project progress and strategy.
  • Work with large datasets to clean, preprocess, and analyze data.
  • Build and deploy statistical and machine learning models to generate actionable insights.
  • Conduct exploratory data analysis (EDA) to uncover trends, patterns, and correlations.
  • Assist in the creation of data visualizations and dashboards for reporting insights.

Requirements

  • Any Graduate / PassOuts / Freshers can apply.
  • Currently pursuing a Bachelor's or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
  • Proficiency in programming languages such as Python, R, or SQL.
  • Strong foundation in statistics, probability, and data analysis techniques.

Benefits

  • Internship Certificate
  • Letter of recommendation
  • Stipend Performance Based
  • Part time work from home (2-3 Hrs per day)
  • 5 days a week, Fully Flexible Shift
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DevLynx

Canibuild is a fast-growing SaaS platform that revolutionizes the residential construction industry by automating design, approval, and sales processes. Backed by Australia's largest hedge fund, Canibuild enables clients across Australia, New Zealand, Canada, and the US to quickly determine the feasibility of construction projects on specific plots of land. The company fosters a culture of innovation and collaboration, providing opportunities for professionals to engage in AI-driven data initiatives and modern data engineering practices.

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