Company Overview: SAIC is a premier technology integrator solving our nation's most complex modernization and readiness challenges across the defense, space, federal civilian, and intelligence markets. Our robust portfolio of offerings includes high-end solutions in systems engineering and integration, enterprise IT, cyber security, AI/ML, and data analytics.
This is a part-time remote job that can be worked anywhere within the U.S.
Key Responsibilities:
- Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions that leverage machine learning and data science techniques.
- Assist in the development and deployment of machine learning models, algorithms, and data pipelines to solve complex problems and optimize processes.
- Conduct data preprocessing, feature engineering, and exploratory data analysis to enhance model performance and insights.
- Participate in designing and implementing experiments to evaluate model performance and iterate on improvements.
- Assist in the integration of machine learning solutions into production systems, ensuring scalability, robustness, and reliability.
- Stay up-to-date with the latest advancements in machine learning, artificial intelligence, and data science to propose innovative ideas and techniques for improving existing systems.
- Contribute to documentation, knowledge sharing, and training to ensure the transfer of technical knowledge within the team.
Qualifications:
- Must be a US Citizen.
- Currently enrolled in an accredited university focusing in Computer Science, Engineering, Mathematics, or a related field.
- Strong foundation in machine learning concepts and techniques, including supervised and unsupervised learning, feature selection, and model evaluation.
- Proficiency in programming languages such as Python, and experience with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Familiarity with data preprocessing, cleaning, and transformation techniques to handle real-world data challenges.
- Basic understanding of statistics and experimental design for evaluating model performance and making data-driven decisions.
- Exposure to version control systems (e.g., Git) and collaborative coding practices.
- Strong problem-solving skills and the ability to work effectively in a team-oriented, fast-paced environment.
- Excellent communication skills to convey complex technical concepts to non-technical stakeholders.
- Previous coursework, projects, or internships in machine learning or data science is a plus.
- Any experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies is advantageous.
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