Principal Data Scientist AI Engineer at SAIC

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Full Time

Start Date

Immediate

Expiry Date

16 Mar, 26

Salary

0.0

Posted On

16 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Engineering, Data Science, Machine Learning, Natural Language Processing, Cloud Engineering, Cybersecurity, Prompt Engineering, Retrieval-Augmented Generation, Predictive Modeling, Statistical Analysis, Data Analysis, Exploratory Data Analysis, Human Centered Design, Agile Methodologies, Data Architecture, Mentoring

Industry

Defense and Space Manufacturing

Description

SAIC is seeking an innovative, hands-on development oriented, AI Engineer / Data Scientist with eight years’ experience in AI/ML/NLP and data modeling to develop prototypes of emerging technologies that solve business problems. The candidate must have cloud engineering experience to ensure integration of the AI/ML/NLP solutions in cloud native environments. This resource will join an energetic and versatile team responsible for addressing the customer challenges through the use of advancing technologies. This is a remote job that can be worked from any location in the continental U.S. Responsibilities: Operate and maintain an innovation lab for the ideation, design, and engineering of cloud native product modernization and innovation using emerging technologies. Develop end-to-end plans for cloud native AI/ML/NLP innovations that are compliant with cybersecurity policies. Develop and engineer pilots, prototypes, and proof of concept solutions to validate emerging technology, modernization, and innovation opportunities. We need a resource that understands modern AI Large Language Models (LLM’s) and their inner workings / architectures / backbone and head. We need a resource experienced in prompt engineering and Retrieval-Augmented Generation (RAG). Implement transparency, traceability, and appropriate security handling in line with best practices and applicable compliance standards including Authorization To Operate (ATO) certifications. Implement predictive models, machine learning algorithms, and statistical analyses to solve complex business problems. Develop low risk implementation plans and present business briefings, recommendations, and demonstrations to executives and technical stakeholders in a clear manner based on data architecture best practices. Collaborate with cross-functional teams including data engineering, product, and business stakeholders to translate requirements into data-driven solutions. Conduct exploratory data analysis and generate actionable insights from large and complex datasets. Support total experience initiatives including customer experience, user experience and human centered design through focus groups, metrics, wireframes, mockups, and information architecture development. Develop reusable code libraries and best practices for modernization and innovation development and deployment. Mentor more junior data scientists and guide the team in adopting the new technologies and techniques. Stay current with advancements in data science, machine learning, AI research and cloud architectures. Support Agile product teams in integrating and operationalizing emerging technologies and data strategies. Analyze data and usage to find patterns and solutions to business challenges. Qualifications: MA/MS degree and 8 years or more of experience in data analytics, data modeling, machine learning, natural language processing, security, and artificial intelligence technologies in cloud native environments (AWS, Azure, Google). Additional experience may be substituted for educational requirements where appropriate. US Citizenship not required, but must have lived in the US for at least two consecutive years. Must be able to obtain a Public Trust.

Responsibilities

The Principal Data Scientist AI Engineer will operate and maintain an innovation lab for cloud native product modernization and innovation. They will develop end-to-end plans for AI/ML/NLP innovations and collaborate with cross-functional teams to translate requirements into data-driven solutions.