| 21 |
Knowledge of computer algorithms. |
Knowledge |
| 75A |
Knowledge of mathematics, including logarithms, trigonometry, linear algebra, calculus, statistics, and operational analysis. |
Knowledge |
| 102 |
Knowledge of programming language structures and logic. |
Knowledge |
| 166 |
Skill in conducting queries and developing algorithms to analyze data structures. |
Skill |
| 477 |
Correct errors by making appropriate changes and rechecking the program to ensure desired results are produced. |
Task |
| 506 |
Design, develop, and modify software systems, using scientific analysis and mathematical models to predict and measure outcome and consequences of design. |
Task |
| 543 |
Develop secure code and error handling. |
Task |
| 764 |
Perform secure programming and identify potential flaws in codes to mitigate vulnerabilities. |
Task |
| 942 |
Knowledge of the organization’s core business/mission processes. |
Knowledge |
| 1000B |
Ensure that AI design and development activities are properly documented and updated. |
Task |
| 5120 |
Conduct hypothesis testing using statistical processes. |
Task |
| 5925 |
Use knowledge of business processes to create or recommend AI solutions. |
Task |
| 5847 |
Assess and address the limitations of methods to deliver machine learning models. |
Task |
| 5854 |
Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data resusability concerns for AI solutions. |
Task |
| 5858 |
Conduct AI risk assessments to ensure models and/or other solutions are performing as designed. |
Task |
| 5859 |
Consider energy implications (graphical processing unit, tensor processing unit, etc.) when designing AI solutions. |
Task |
| 5870 |
Design and develop continuous integration/continuous delivery (CI/CD) in a containerized or other reproducible computing environment to support the machine learning life cycle. |
Task |
| 5871 |
Design and develop machine learning models to achieve organizational objectives. |
Task |
| 5872 |
Design, develop, and implement AI tools and techniques to achieve organizational objectives. |
Task |
| 5873 |
Determine methods and metrics for quantitative and qualitative measurement of AI risks so that sensitivity, specificity, likelihood, confidence levels, and other metrics are identified, documented, and applied. |
Task |
| 5889 |
Identify and submit exemplary AI use cases, best practices, failure modes, and risk mitigation strategies, including after-action reports. |
Task |
| 5893 |
Implement Responsible AI best practices and standards within AI solutions according to the DoD AI Ethical Principles, Responsible AI Guidelines, and/or any other pertinent laws. |
Task |
| 5896 |
Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI. |
Task |
| 5915 |
Research the latest machine learning and AI tools, techniques, and best practices. |
Task |
| 5926 |
Use models and other methods for evaluating AI performance. |
Task |
| 5927 |
Write and document reproducible code. |
Task |
| 6060 |
Ability to collect, verify, and validate test data. |
Ability |
| 6290 |
Knowledge of how to leverage government research and development centers, think tanks, academic research, and industry systems. |
Knowledge |
| 6311 |
Knowledge of machine learning theory and principles. |
Knowledge |
| 6760 |
Skill in writing scripts using R, Python, PIG, HIVE, SQL, etc. |
Skill |
| 7003 |
Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation solutions. |
Knowledge |
| 7009 |
Knowledge of coding and scripting in languages that support AI development and use. |
Knowledge |
| 7011 |
Knowledge of current AI and machine learning systems design and performance analysis models, algorithms, and tools. |
Knowledge |
| 7020 |
Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable). |
Knowledge |
| 7021 |
Knowledge of emerging trends and future use cases of AI. |
Knowledge |
| 7022 |
Knowledge of how AI adoption can assist developers with service-oriented design. |
Knowledge |
| 7024 |
Knowledge of how AI is developed and operated. |
Knowledge |
| 7025 |
Knowledge of how AI solutions integrate with cloud or other IT infrastructure. |
Knowledge |
| 7026 |
Knowledge of how commercial and federal solutions solve Defense-related data environment and platform challenges. |
Knowledge |
| 7028 |
Knowledge of how to automate development, testing, security, and deployment of AI/machine learning-enabled software to the DoD. |
Knowledge |
| 7029 |
Knowledge of how to collect, store, and monitor data. |
Knowledge |
| 7031 |
Knowledge of how to structure and display data. |
Knowledge |
| 7032 |
Knowledge of how to use data to tell a story. |
Knowledge |
| 7036 |
Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government. |
Knowledge |
| 7037 |
Knowledge of machine learning operations (MLOps) processes and best practices. |
Knowledge |
| 7038 |
Knowledge of metrics to evaluate the effectiveness of machine learning models. |
Knowledge |
| 7040 |
Knowledge of Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability considerations for AI solutions. |
Knowledge |
| 7041 |
Knowledge of remedies against unintended bias in AI solutions. |
Knowledge |
| 7044 |
Knowledge of testing, evaluation, validation, and verification (T&E V&V) tools and procedures to ensure systems are working as intended. |
Knowledge |
| 7045 |
Knowledge of the AI lifecycle. |
Knowledge |
| 7046 |
Knowledge of the basic requirements for the successful delivery of AI solutions. |
Knowledge |
| 7048 |
Knowledge of the benefits and limitations of AI capabilities. |
Knowledge |
| 7049 |
Knowledge of the latest machine learning and AI tools, techniques, and best practices. |
Knowledge |
| 7050 |
Knowledge of the nature and function of technology platforms and tools used to create and employ AI. |
Knowledge |
| 7051 |
Knowledge of the possible impacts of machine learning blind spots and edge cases. |
Knowledge |
| 7055 |
Skill in analyzing the output from machine learning models. |
Skill |
| 7057 |
Skill in building and deploying machine learning models. |
Skill |
| 7059 |
Skill in creating machine learning models. |
Skill |
| 7065 |
Skill in explaining AI concepts and terminology. |
Skill |
| 7067 |
Skill in identifying low-probability, high-impact risks in machine learning training data sets. |
Skill |
| 7069 |
Skill in identifying risk over the lifespan of an AI solution. |
Skill |
| 7071 |
Skill in labeling data to make it more discoverable and understandable. |
Skill |
| 7075 |
Skill in testing and evaluating machine learning algorithms or AI solutions. |
Skill |
