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Internships in Data Science, Operations Research, Math and Statistics

Salary

$33k - $50k

Min Experience

0 years

Location

Gaithersburg, MD

JobType

internship

About the job

Info This job is sourced from a job board

About the role

At MITRE, we are dedicated to addressing our nation's most pressing challenges through innovative technology and data-driven solutions. As an intern in our data science team, you will have the opportunity to work alongside experienced professionals in a collaborative environment that values diversity, inclusion, and professional growth. Your role will involve applying your academic knowledge in real-world scenarios, contributing to projects that enhance operational effectiveness and efficiency across various sectors, including cybersecurity, healthcare, and defense. You will engage in a variety of tasks that may include data analysis and visualization, augmented decision-making, and the development of trusted analytics. Your work will help create robust data engineering architectures and machine learning algorithms that empower users to harness the full potential of big data. This internship is designed to provide you with hands-on experience in applied mathematics, statistics, optimization, and systems modeling, all while fostering your understanding of human decision-making processes in conjunction with machine capabilities. As part of our commitment to your professional development, you will receive mentorship and guidance from seasoned experts in the field. This internship not only aims to enhance your technical skills but also to instill a sense of purpose as you contribute to projects that have a meaningful impact on society. We believe that by working together, we can create a safer, healthier, and more secure world.

About the company

MITRE is a not-for-profit organization that operates multiple federally funded research and development centers. MITRE's mission is to solve problems for a safer world.

Skills

data analysis
data visualization
optimization
statistics
applied mathematics
systems modeling