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.
Requirements:
- 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.
Responsibility:
- 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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