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Job Description

Job Description :

Join our team as a Data Science Engineer Intern, where you'll gain hands-on experience in building machine learning models, analyzing large datasets, and implementing data science solutions. Contribute to impactful projects while learning from experienced professionals in the field.

We are looking for INTERNS.

Responsibility:

  • Assist in developing machine learning models for predictive analytics and business intelligence.
  • Analyze large datasets to uncover trends, patterns, and insights.
  • Collaborate with cross-functional teams to implement data science solutions into products and systems.
  • Help preprocess, clean, and structure raw data for analysis and model development.
  • Contribute to building data pipelines using cloud technologies and big data tools.
  • Monitor and test model performance to optimize algorithms.
  • Assist in creating data visualizations and dashboards to present insights to stakeholders.
  • Keep up with the latest trends in data science and machine learning techniques.

Requirements:

  • Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field (or currently pursuing).
  • Basic knowledge of Python or R for data analysis and machine learning.
  • Familiarity with machine learning libraries like TensorFlow, Scikit-Learn, or PyTorch.
  • Good understanding of SQL and experience with relational databases.
  • Familiarity with cloud computing platforms (AWS, GCP, Azure).
  • Basic knowledge of data visualization tools (e.g., Tableau, PowerBI, or Matplotlib).
  • Strong analytical and problem-solving skills.
  • Interest in working with large datasets and big data tools (Hadoop, Spark).
  • Good verbal communication skills.

More Info

About Company

Incorporated in 2014, Intuitive Apps Inc. has been deeply rooted in a legacy of transforming the industry and the way people experience technology consulting, and we’re committed to deliver a seamless, personalized experience to save costs, bring efficiencies, and making them robust.

Job ID: 103730525

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