Wastefull Insights Internship 2024 For Data Engineer Intern | Location Vadodara

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Wastefull Insights Internship 2024: Wastefull Insights Internship 2024 for its Data Engineer Intern. Wastefull Insights Drive organizes off-campus drives for candidates in any batch. To apply, candidates should have a Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field. if you are interested, please apply as soon as possible.

Company Name: Wastefull Insights

Role: Data Engineer Intern

Location: Vadodara

Experience: Freshers

Qualification: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.

Batch: Any

Job Type: Full Time

Salary: Best in Industry

Wastefull Insights Internship 2024

Job Description:

  • We are seeking a highly skilled and motivated Data Engineer to join our innovative team.
  • The ideal candidate will possess a deep understanding of data engineering principles and practices, particularly in preparing and managing datasets for deep learning models.
  • The candidate should also have the ability to interact with and lead an annotation team to ensure the highest quality data for AI model training.


  • Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
  • Proven experience as a Data Engineer or in a similar role.
  • Strong understanding of deep learning models and their data requirements.
  • Experience with leading and managing an annotation team.
  • Proficiency in preprocessing image datasets.
  • Familiarity with unsupervised learning methods (bonus point).
  • Proficient in programming languages such as Python and SQL, and data manipulation libraries (e.g., pandas, NumPy).
  • Experience with data pipeline tools and frameworks such as Apache Airflow, Kafka, or similar.
  • Knowledge of image processing techniques and libraries (e.g., OpenCV, PIL).
  • Experience with cloud platforms such as AWS, GCP, or Azure for data storage and processing.


  • Data Collaboration: Work closely with AI engineers to understand data requirements for various deep learning models.
  • Annotation Leadership: Lead the annotation team, providing clear instructions and ensuring high-quality annotations.
  • Annotation Guidance: Explain the importance of specific annotation precautions to prevent model confusion.
  • Data Preprocessing: Preprocess image datasets, including data cleaning, transformation, and augmentation to optimize them for deep learning applications.
  • Data Pipeline Management: Implement and manage data pipelines to ensure efficient data flow from collection to model training.
  • Unsupervised Learning: Utilize unsupervised learning methods as needed to enhance data quality and model performance (bonus point).

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