G2 Ops, Inc.
1 month ago Be among the first 25 applicants
Location:
Virginia Beach, VA at our wonderful G2 Ops office. Work Setting:
In person, some remote opportunity and/or flexible working hours, not a fully remote position. Looking to Start:
August/September2025 Salary Range:
$130-$150K plus benefits Openings:
1 Full-Time Role Years of Industry Experience:
5 +
years of relevant experience Security Clearance Requirement:
Must be able to obtain and maintain Active DoD Secret Clearance Knowledge Requirements and Qualifications
Bachelor\'s or Master\'s of Science in Data Science, Computer Science, AI/ML, Software Engineering, Computational Science, Information Systems (with Data Analytics or AI Concentration) or related degree Technical Skills
Programming & Data Tools
Proficiency in Python and R for machine learning and data analysis. Experience with libraries like Pandas, NumPy, and scikit-learn for data manipulation and model building. Familiarity with data visualization tools such as Matplotlib or Plotly.
Experience developing and fine-tuning machine learning models, including supervised and unsupervised learning. Applied knowledge of deep learning frameworks such as TensorFlow or PyTorch. Familiarity with transformer-based architectures (e.g., BERT, GPT) and usage of Hugging Face Transformers. Ability to design and implement basic model training pipelines, including data ingestion, preprocessing, and evaluation. Software Development Lifecycle (SDLC)
Participation in the end-to-end ML lifecycle: data prep, model training, evaluation, deployment, and monitoring. Familiarity with version control systems (e.g., Git) and basic software engineering practices.
Database and Data Handling Working knowledge of SQL and NoSQL databases (e.g., MongoDB, Apache Cassandra, Oracle, mySQL). Experience building or consuming ETL pipelines for structured data preparation. Bonus Skills Hands-on experience with large language models (LLMs) using OpenAI API, RAG, or embedding models. Familiarity with prompt engineering for model fine-tuning or inference optimization. Understanding of vector databases (e.g., Qdrant, Pinecone) and semantic search techniques. Use of MLOps tools for CI/CD pipelines in AI (e.g., MLflow, Kubeflow, SageMaker). AI for Systems Engineering
Experience working with SysML, MBSE tools, or digital engineering pipelines. Understanding of how to map or extract system design intent from technical documentation using NLP
Experience creating interactive dashboards using tools like Tableau, Streamlit, or Power BI Responsibilities
Develop and deploy machine learning models that support the automation of SysML model generation from static, text-based system documentation Preprocess and curate training datasets using structured and unstructured engineering content, ensuring quality and consistency across iterations Implement and evaluate natural language processing (NLP) techniques, including fine-tuning transformer-based models (e.g., BERT, GPT) to extract relevant system design information Build and maintain scalable ML pipelines, including data ingestion, feature engineering, training, validation, and deployment workflows Collaborate with software engineers, systems engineers, and domain experts to translate technical documentation into structured, model-ready data Analyze and optimize model performance, applying statistical techniques and metrics to validate reliability, accuracy, and generalization to unseen documentation Support the integration of ML models into operational tools, including MBSE environments, and assist in testing, debugging, and refining based on user feedback Document technical approaches, model training procedures, and experimental results to support reproducibility, knowledge sharing, and contract deliverables Contribute to research and innovation, staying current with AI/ML advancements, and proposing techniques that enhance automation accuracy or efficiency Ensure compliance with internal development standards, including data handling, version control, model tracking, and secure code practices Company Culture and Benefits
What makes someone choose one company over another?
Pay, benefits, training, work satisfaction, culture? At G2 Ops, we offer competitive pay and benefits and a strong culture. We aim to avoid treating employees as just a payroll number, emphasizing teamwork and cross-training opportunities. Embracing AI . G2 Ops stays ahead by embracing AI technologies and inviting team members to explore and implement AI solutions that improve internal workflows and client outcomes. Salary and Benefits . The annual salary range for this position is $130,000 to $150,000, with a competitive benefits package. Benefits include a value range stated in the posting and annual performance-based recognition programs. Remote and On-Site Work . Remote work is not guaranteed due to project classification levels. We offer a flexible schedule and require secure access due to defense contracting. You will have a desk at our G2 Ops office and may telework with prior approval. Candidate Fit . We seek a highly motivated, collaborative Data Scientist who thrives in a multidisciplinary environment, contributing across data modeling, software engineering, machine learning, and AI to transform how information is structured and applied. Clearance . DoD Secret level clearance is preferred; active clearance is a plus. Additional Note . This is a full-time position. Outside employment may present conflicts with company interests. Closing . We look forward to learning more about you.
#J-18808-Ljbffr
Virginia Beach, VA at our wonderful G2 Ops office. Work Setting:
In person, some remote opportunity and/or flexible working hours, not a fully remote position. Looking to Start:
August/September2025 Salary Range:
$130-$150K plus benefits Openings:
1 Full-Time Role Years of Industry Experience:
5 +
years of relevant experience Security Clearance Requirement:
Must be able to obtain and maintain Active DoD Secret Clearance Knowledge Requirements and Qualifications
Bachelor\'s or Master\'s of Science in Data Science, Computer Science, AI/ML, Software Engineering, Computational Science, Information Systems (with Data Analytics or AI Concentration) or related degree Technical Skills
Programming & Data Tools
Proficiency in Python and R for machine learning and data analysis. Experience with libraries like Pandas, NumPy, and scikit-learn for data manipulation and model building. Familiarity with data visualization tools such as Matplotlib or Plotly.
Experience developing and fine-tuning machine learning models, including supervised and unsupervised learning. Applied knowledge of deep learning frameworks such as TensorFlow or PyTorch. Familiarity with transformer-based architectures (e.g., BERT, GPT) and usage of Hugging Face Transformers. Ability to design and implement basic model training pipelines, including data ingestion, preprocessing, and evaluation. Software Development Lifecycle (SDLC)
Participation in the end-to-end ML lifecycle: data prep, model training, evaluation, deployment, and monitoring. Familiarity with version control systems (e.g., Git) and basic software engineering practices.
Database and Data Handling Working knowledge of SQL and NoSQL databases (e.g., MongoDB, Apache Cassandra, Oracle, mySQL). Experience building or consuming ETL pipelines for structured data preparation. Bonus Skills Hands-on experience with large language models (LLMs) using OpenAI API, RAG, or embedding models. Familiarity with prompt engineering for model fine-tuning or inference optimization. Understanding of vector databases (e.g., Qdrant, Pinecone) and semantic search techniques. Use of MLOps tools for CI/CD pipelines in AI (e.g., MLflow, Kubeflow, SageMaker). AI for Systems Engineering
Experience working with SysML, MBSE tools, or digital engineering pipelines. Understanding of how to map or extract system design intent from technical documentation using NLP
Experience creating interactive dashboards using tools like Tableau, Streamlit, or Power BI Responsibilities
Develop and deploy machine learning models that support the automation of SysML model generation from static, text-based system documentation Preprocess and curate training datasets using structured and unstructured engineering content, ensuring quality and consistency across iterations Implement and evaluate natural language processing (NLP) techniques, including fine-tuning transformer-based models (e.g., BERT, GPT) to extract relevant system design information Build and maintain scalable ML pipelines, including data ingestion, feature engineering, training, validation, and deployment workflows Collaborate with software engineers, systems engineers, and domain experts to translate technical documentation into structured, model-ready data Analyze and optimize model performance, applying statistical techniques and metrics to validate reliability, accuracy, and generalization to unseen documentation Support the integration of ML models into operational tools, including MBSE environments, and assist in testing, debugging, and refining based on user feedback Document technical approaches, model training procedures, and experimental results to support reproducibility, knowledge sharing, and contract deliverables Contribute to research and innovation, staying current with AI/ML advancements, and proposing techniques that enhance automation accuracy or efficiency Ensure compliance with internal development standards, including data handling, version control, model tracking, and secure code practices Company Culture and Benefits
What makes someone choose one company over another?
Pay, benefits, training, work satisfaction, culture? At G2 Ops, we offer competitive pay and benefits and a strong culture. We aim to avoid treating employees as just a payroll number, emphasizing teamwork and cross-training opportunities. Embracing AI . G2 Ops stays ahead by embracing AI technologies and inviting team members to explore and implement AI solutions that improve internal workflows and client outcomes. Salary and Benefits . The annual salary range for this position is $130,000 to $150,000, with a competitive benefits package. Benefits include a value range stated in the posting and annual performance-based recognition programs. Remote and On-Site Work . Remote work is not guaranteed due to project classification levels. We offer a flexible schedule and require secure access due to defense contracting. You will have a desk at our G2 Ops office and may telework with prior approval. Candidate Fit . We seek a highly motivated, collaborative Data Scientist who thrives in a multidisciplinary environment, contributing across data modeling, software engineering, machine learning, and AI to transform how information is structured and applied. Clearance . DoD Secret level clearance is preferred; active clearance is a plus. Additional Note . This is a full-time position. Outside employment may present conflicts with company interests. Closing . We look forward to learning more about you.
#J-18808-Ljbffr