Data Scientist Intern – Summer 2025

Data Scientist Intern – Summer 2025

Job Category: Intern/Co-op

Time Type: Part time

Minimum Clearance Required to Start: None

Employee Type: Part-Time On-Call

Percentage of Travel Required: None

Type of Travel: None

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The Opportunity:

CACI is currently seeking a summer 2025 intern for a Data Scientist opportunity. This position will be remote, with core hours from 9 a.m. to 5 p.m. Please note that while the role is remote, candidates must be located in the Reston, VA area to participate in team activities and summer internship events. 

Responsibilities:

• Join the Enterprise Digital Transformation and Analytics Team focused on corporate AI projects.
• Develop custom solutions to help the businesses drive growth and optimize business efficiency.
• Develop innovative solutions driven by exploratory data analysis using multiple datasets and leveraging machine learning technologies and advanced techniques. 
• You will bring your expertise, curiosity, innovation, and customer centric approach to employ a variety of programming languages and tools for data integrations, transforming the data into database views and creating custom, repeatable predictive models.  
• You will be part of a collaborative team working on various tasks such as developing machine learning models, leveraging large language models, analyzing data sets, and creating data visualizations.
• In addition, you will leverage the capabilities of AWS and the data analytics services to include the use of Amazon Athena, Amazon Redshift, Amazon Glue, etc. along with other related toolsets.
• Responsible for innovative data and business solutions by applying your knowledge of statistics, machine learning, programming, data modeling, visualization, and advanced mathematics to recognize patterns and identify opportunities. 
• As part of the customer centric methodology, you will work directly with senior customers to understand the business, processes and how the data is used for decision activities and pose business questions to help drive data discoveries, data design, and prototypes. 
• Use an agile, analytical approach to design, develop, and evaluate descriptive and predictive models and advanced algorithms on both structured and unstructured data.
• Generate and test hypotheses and derive patterns, trends, and correlations.   
• Build repeatable machine learning systems to automate predictive models. 
• Deploy large language models (LLMs) to drive advanced language processing and analysis tasks.
• Effectively consult and communicate with senior leaders of the results of the data models.
• Use a combination of tools in AWS, software languages, and Python libraries to develop ML algorithms to analyze huge volumes of historical data, run tests, perform statistical analyses, interpret results, and document machine learning processes.
• Be able to review data diagrams to understand entity relationships and data values that define uniqueness, analyze data to confirm relationships and identify potential data quality issues.
• Use critical thinking to assess deficiencies in existing solutions and provide recommendations for improvements.

 

Qualifications:

Required:

• Knowledge of using a variety of machine learning techniques: linear regression, logistic regression, random forests, gradient boosting, neural networks, natural language processing, clustering, and classification algorithms.
• Python programming experience
• Experience with machine learning and statistical libraries such as Scikit-Learn, SciPy, NumPy and Pandas; familiarity with modern deep learning frameworks, such as TensorFlow, Keras, and/or PyTorch.
• Ability to visualize and present data and results in a clear and intuitive way using tools such as Matplotlib, Plotly, Seaborn, and Jupyter Notebooks.
• Experience with SQL and other various non-SQL databases and object stores
• Experience in deploying and leveraging LLMs, including prompt engineering
• Analytical, organizational and data management skills
• Ability to conduct in-depth data analyses to assist in validating business assumptions and investigating issues.  A firm understanding of database concepts and entity relationships
• Ability