Data Science

Altagrove LLC•Norfolk, VA

About The Position

Altagrove delivers smart and innovative technology solutions that create competitive advantages for our customers and their missions. Our focus areas include Space, Connectivity, Cyber, Cloud, Analytics, and Research & Development. As we continue to grow, Altagrove is actively recruiting for a Data Scientist to join our energetic and entrepreneurial team that is executing on a variety of projects that are technology oriented. A successful candidate will bring a core area of expertise and a passion for learning and implementing new ideas in a start-up environment.

Requirements

Experience developing and deploying advanced analytics, machine learning, artificial intelligence, or enterprise data engineering solutions within government, defense, intelligence, or highly regulated environments.
Strong proficiency in Python and experience with modern data science and engineering frameworks.
Experience with machine learning frameworks such as Scikit-Learn, TensorFlow, PyTorch, or similar technologies.
Experience designing and maintaining enterprise data pipelines, ETL/ELT workflows, and data integration architectures.
Familiarity with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), vector databases, embeddings, and prompt engineering concepts.
Experience working with SQL, NoSQL, data lakes, data warehouses, and cloud-native data platforms.
Experience with AWS, Azure, Google Cloud, or hybrid cloud environments.
Understanding of data governance, metadata management, data quality, security, and compliance principles.
Strong analytical, problem-solving, and communication skills.
Ability to collaborate effectively across multidisciplinary engineering and mission teams.
Bachelor’s or Master’s degree in Data Science, Computer Science, Data Engineering, Mathematics, Statistics, Engineering, Information Systems, or a related discipline.
Active Secret Clearance required; TS/SCI or NATO Secret preferred.
Exceptional attention to detail and organizational skills.

Nice To Haves

Experience with containerization technologies, Kubernetes, DevSecOps practices, and Infrastructure-as-Code is highly desirable.
Experience supporting NATO, DoD, Intelligence Community, or other national security organizations is highly desired.

Responsibilities

Design, develop, deploy, and maintain AI-enabled analytics solutions supporting operational and strategic mission objectives.
Build and optimize enterprise data pipelines, ingestion frameworks, transformation workflows, and integration services supporting analytics and AI platforms.
Develop machine learning models, predictive analytics capabilities, and decision-support solutions using structured and unstructured data.
Design and implement Large Language Model (LLM) solutions, Retrieval-Augmented Generation (RAG) architectures, vector databases, and AI-enabled knowledge management capabilities.
Develop scalable data architectures, metadata enrichment pipelines, indexing services, and retrieval systems supporting enterprise knowledge exploitation.
Collaborate with mission stakeholders, engineers, and technical teams to identify high-value AI and data-driven use cases.
Conduct data exploration, feature engineering, model training, validation, testing, and performance optimization activities.
Design and implement ETL/ELT processes supporting operational, analytical, and machine learning workloads.
Develop dashboards, visualizations, reports, and analytical products that communicate insights to technical and non-technical stakeholders.
Support cloud-native and hybrid data environments leveraging AWS, Azure, Kubernetes, and modern data engineering technologies.
Implement data quality controls, monitoring solutions, security controls, and governance practices across enterprise data environments.
Work closely with Cloud Engineers, DevSecOps Engineers, and Software Developers to support end-to-end solution delivery.
Research emerging AI, machine learning, and data engineering technologies and recommend innovative applications for customer missions.
Support technical documentation, architecture development, operational procedures, training materials, and knowledge transfer activities.