Data Scientist / Machine Learning Engineer

Trabus Technologies•San Diego, CA

About The Position

Data Scientist / Machine Learning Engineer About TRABUS Technologies TRABUS Technologies (TRABUS) is a minority-owned, Service-Disabled Veteran-Owned Small Business (SDVOSB) headquartered in San Diego, California. Since 2010, TRABUS has provided advanced wireless technology solutions, cybersecurity expertise, artificial intelligence capabilities, and engineering support to federal and commercial clients. Recognized on the Inc. 5000 list for seven consecutive years, TRABUS is a dynamic, forward-leaning organization committed to solving complex, real-world challenges and delivering mission-ready solutions that meet and exceed customer expectations. Position Overview TRABUS Technologies is seeking a highly motivated Data Scientist / Machine Learning Engineer to join our growing Artificial Intelligence and Data Science team. In this role, you will combine machine learning, data science, and software engineering skills to support a variety of AI-based projects, including resource planning for ship maintenance, marine transportation, and climate and environmental informatics. The successful candidate will develop machine learning models that provide predictive insights and analytics for robust, web-based, full-stack AI applications supporting both government customers — including the Department of Defense (DoD), Department of Transportation (DoT), and National Oceanic and Atmospheric Administration (NOAA) — and commercial customers.

Requirements

Minimum of 2 years of programming experience using Python .
Completed coursework in Machine Learning, Deep Learning, Algorithm Design, and Data Science.
Knowledge and experience with Python data science and machine learning libraries such as: Pandas scikit-learn Keras TensorFlow PyTorch
Strong Python programming skills with the ability to process and analyze large datasets and data streams.
Understanding of databases, data structures, and API-based ETL operations.
Strong working knowledge of Linux or Unix-based operating systems.
Familiarity with Python web frameworks such as FastAPI, Django, and Flask.
Experience working with Git repositories and version control.
Ability to work collaboratively within multidisciplinary technical teams.
U.S. Citizenship is required.
Must be able to obtain and maintain a final DoD-adjudicated Secret security clearance .

Nice To Haves

Current DoD-adjudicated Secret security clearance.
Strong knowledge of DevOps tools, processes, and best practices.
Experience working with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
Familiarity with Agile software development methodologies.
Experience working within Agile development environments.
Experience with Continuous Integration/Continuous Deployment (CI/CD) pipelines.
Proficiency with Microsoft Office applications.
Strong written and verbal communication skills.
Strong attention to detail and commitment to procedural compliance.
Ability to manage multiple responsibilities and priorities while meeting established deadlines.

Responsibilities

Develop code to extract, clean, transform, and preprocess training datasets from disparate data formats, sources, and APIs.
Research, evaluate, select, and develop appropriate Machine Learning (ML) and Deep Learning (DL) algorithms and models.
Apply AI/ML techniques across diverse data domains, including Navy, transportation, environmental sciences, healthcare, and public safety.
Collaborate with the TRABUS Data Science team to develop scalable AI solutions incorporating technologies such as: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Prompt Engineering Computer Vision Natural Language Processing (NLP)
Work alongside AI/ML and DevOps engineers to develop automated AI workflows that continuously generate predictions and inferences.
Develop validation and testing routines to measure model performance and evaluate the quality and accuracy of predictions.
Analyze large datasets and data streams to identify patterns, trends, and actionable insights.
Support the integration of machine learning models into web-based and full-stack AI applications.
Collaborate with technical teams throughout the development, testing, integration, and deployment lifecycle.