Data Engineer Intern

The Commercial and International Sector at Leidos currently has an opening for a Data Engineer Intern to work in our Tewksbury, MA office. Leidos is at the forefront of AI and ML technologies, dedicated to solving complex problems and improving the world through our innovation. Our Computer Vision ML teams are pioneering new algorithms and models that are revolutionizing how machines interpret and understand visual data. Join us to be part of this exciting journey!

Primary Responsibilities

• Data preparation: Collect, clean, and preprocess large datasets to prepare them for analysis and modeling by our ML engineers.
• Pipeline Development: Assist in development and optimization of data pipelines for more efficient data processing, storage, and retrieval, ensuring data quality and accessibility.
• Performance Monitoring: Monitor model performance and data pipeline health, identifying and resolving issues to ensure smooth operations.
• Research Support: Support research efforts by conducting data exploration and analysis to uncover insights and improve model performance.
• Maintain detailed documentation of data pipelines, datasets, and processes for future reference, governance, and compliance.
• Work closely with ML engineers and data scientists to understand their data needs and support them in training and deploying computer vision models.

Basic Qualifications

• Currently pursuing Bachelor’s in computer science, data science, engineering, mathematics, or related technical field.
• Strong programming skills in python, especially with data manipulation and processing libraries (e.g. pandas, numpy).
• Understanding of data structures. Familiarity with data structures and algorithms essential for efficient data processing.
• Basic knowledge of database management systems (e.g. SQL, NoSQL).
• Ability to work effectively in a collaborative team environment.

Preferred Qualifications

• Experience with big data technologies. (e.g. Spark, Hadoop).
• Experience with cloud platform services. (e.g. GCP, Azure, AWS).
• Basic understanding of machine learning concepts and experience with ML libraries. (e.g. Pytorch, Tensorflow).
• Ability to communicate complex data and ML concepts to non-expert stakeholders.