Data Science Intern

Remote
Full Time
Student (College)

Data Science Intern
Location: Remote
Part Time


**MUST BE A US CITIZEN TO APPLY. Those who are not US citizens will not be considered.**



Overview:
We are seeking a motivated and proactive Data Science Intern to join our team on a part-time basis. The ideal candidate is a self-starter who thrives on tackling challenging, real-world data science problems and has a strong focus on machine learning.
 
Key Responsibilities:
  • Design, implement, and evaluate machine learning models, including classical methods (e.g., logistic regression, random forests, gradient boosting) and deep learning methods (e.g., LSTMs, GRUs, CNNs).
  • Perform statistical analyses such as sampling, correlation studies, regression, and hypothesis testing.
  • Collaborate with cross-functional teams to understand business problems and propose data-driven solutions.
  • Document code, methodologies, and results clearly to facilitate knowledge sharing and reproducibility.
  • Continuously research and implement new methods and tools to improve model performance and efficiency.
 
Required Skills & Qualifications:
  • Strong proficiency in Python, PyTorch, and Scikit-learn, including object-oriented programming and writing clean, well-documented code.
  • Hands-on experience with training, testing, and evaluating machine learning models.
  • Solid understanding of statistical concepts and data analysis techniques.
  • Excellent problem-solving abilities and analytical thinking.
  • Ability to work effectively in a team environment.
 
Preferred Qualifications:
  • Pursuing advanced degree in quantitative discipline like math, physics, economics, or engineering.
  • Cumulative GPA of 3.5 or higher.
  • Research experience or involvement in challenging data science projects.
 
What You'll Gain:
  • Practical experience working on impactful, real-world data science projects.
  • Exposure to advanced machine learning techniques and commercial applications.
  • Flexible part-time schedule to accommodate your academic commitments.
  • Potentially contribute to work publishable in conferences. 
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