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Students are required to include a cover letter outlining their fit for the position they are applying.

Data Visualization Analyst (Volunteer) – Fall Semester Recruitment (Extended Deadline: 10/18/2024)

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Position Overview:

As a Data Visualization Analyst (Volunteer), you will be instrumental in analyzing complex datasets and transforming them into clear, impactful visualizations. Collaborating with researchers, faculty, and fellow interns, you will help uncover valuable insights and create compelling visual representations. This is an excellent opportunity to apply your analytical and visualization expertise while contributing to meaningful, research-driven projects.

Key Responsibilities:

  • Collect, clean, and preprocess data from various sources to ensure accuracy and consistency.
  • Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies in data.
  • Analyze datasets and generate actionable insights using statistical methods and data analysis techniques.
  • Develop and maintain dashboards and reports to track key performance indicators (KPIs) and metrics.
  • Utilize data storytelling techniques to communicate findings in an engaging and meaningful way.
  • Implement best practices in data visualization to ensure clarity, accuracy, and accessibility.
  • Stay current with new trends and advancements in data analysis and visualization, recommending innovative solutions where applicable.
  • Document visualization processes and methodologies to ensure reproducibility and ease of collaboration.

Qualifications:

  • Must be a student currently enrolled at the University of Illinois, Urbana-Champaign.
  • Familiarity with data analysis tools and programming languages such as Python, or R.
  • Proficiency in data visualization tools such as Tableau, or Power BI.
  • Strong understanding of statistical techniques and methodologies.
  • Experience with data cleaning and preprocessing techniques.
  • Strong problem-solving skills with the ability to interpret complex data in a clear and meaningful way.
  • Strong attention to detail and ability to work with minimal supervision.
  • Good communication skills, with the ability to present data findings to non-technical stakeholders.
  • A background in data science, statistics, or a related field is preferred.

Benefits:

  • Gain hands-on experience in data analysis and visualization in an academic research setting.
  • Contribute to high-impact projects and research initiatives.
  • Fully remote with flexible work hours.
  • Opportunity to network with researchers, faculty, and data science professionals.
Machine Learning Engineer (Volunteer) – Fall Semester Recruitment (Extended Deadline: 10/18/2024)

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Position Overview:

As a Machine Learning Engineer, you will have the opportunity to work closely with researchers, faculty, and fellow interns to implement machine learning solutions for complex research problems. This is an excellent chance to apply your ML skills in a real-world academic setting and contribute to cutting-edge research that drives innovation.

Key Responsibilities:

  • Collaborate with researchers to design, develop, and implement machine learning models and algorithms.
  • Assist in the preparation and processing of large datasets for training and testing purposes.
  • Optimize models for performance, scalability, and deployment in research environments.
  • Provide technical support for research teams in terms of data acquisition, pre-processing, and computational infrastructure.
  • Stay updated with the latest machine learning techniques, tools, and trends.
  • Document processes, methodologies, and findings for use by the research community.

Qualifications:

  • Must be a student currently enrolled at the University of Illinois, Urbana-Champaign.
  • Experience in machine learning and data science techniques (e.g., supervised/unsupervised learning, deep learning, reinforcement learning).
  • Proficiency in Python and machine learning frameworks such as TensorFlow, Keras, PyTorch, or Scikit-learn.
  • Knowledge of data processing libraries such as Pandas and NumPy.
  • Strong analytical and problem-solving skills with the ability to interpret complex data.
  • Familiarity with business data or an interest in applying machine learning to business research is a plus.
  • Ability to work independently and in a team environment.
  • Strong communication skills for collaborating with non-technical stakeholders.
  • A background in data science, statistics, or a related field is preferred.

Benefits:

  • Gain hands-on experience with real-world machine learning applications in an academic research setting.
  • Opportunity to contribute to high-impact projects and research publications.
  • Fully remote with flexible work hours.
  • Networking opportunities with researchers, faculty, and data science professionals.