Jobs · Education · Maryland

Teaching Fellow EEI In-Person (Explore Engineering Innovation)

The Johns Hopkins University · Baltimore, MD · 5 mo ago
EducationInternship

About the role

The role involves managing a team of researchers and overseeing projects related to advanced data analytics and machine learning applications in healthcare.

Responsibilities

  • Oversee a team of data scientists and engineers in developing innovative solutions using machine learning and big data technologies.
  • Collaborate with cross-functional teams to define project scope, objectives, and deliverables.
  • Lead the implementation of predictive models and algorithms to improve patient outcomes and operational efficiencies.
  • Ensure compliance with regulatory standards and ethical guidelines in all data-related activities.

Requirements

  • A minimum of 5 years of experience in data science, machine learning, or related fields.
  • Proven track record of leading and mentoring a team of data professionals.
  • Experience with popular machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Strong understanding of statistical methods and data analysis techniques.
  • Excellent communication and interpersonal skills to facilitate collaboration across departments.
  • Qualifications

    • Master's degree in Computer Science, Statistics, Mathematics, or a related field.
    • Professional certifications in data science or machine learning preferred.
    • Experience with cloud-based platforms like AWS, Azure, or Google Cloud.

    Skills

    • Proficiency in Python and R programming languages.
    • Knowledge of SQL and database management systems.
    • Experience with data visualization tools such as Tableau or PowerBI.
    • Ability to manage and analyze large datasets efficiently.

    Benefits

    Competitive compensation package, comprehensive health benefits, generous vacation time, and professional development opportunities.

    Pay

    $80,000 - $100,000 annually, commensurate with experience.

    Schedule

    Full-time, Monday through Friday, 9 AM to 5 PM.

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