Data/AI (DA)
DCWF 623
AI/ML Specialist
Designs, develops, and modifies AI applications, tools, and/or other solutions to enable successful accomplishment of mission objectives.
Tasks
The concrete work activities defined for this role in the DCWF v5.1 spreadsheet. Core tasks are required for the role; additional tasks are associated but not mandatory.
- T1000B additional Ensure that AI design and development activities are properly documented and updated.
- T477 additional Correct errors by making appropriate changes and rechecking the program to ensure desired results are produced.
- T506 additional Design, develop, and modify software systems, using scientific analysis and mathematical models to predict and measure outcome and consequences of design.
- T5120 additional Conduct hypothesis testing using statistical processes.
- T543 additional Develop secure code and error handling.
- T5847 additional Assess and address the limitations of methods to deliver machine learning models.
- T5854 additional Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability concerns for AI solutions.
- T5858 additional Conduct AI risk assessments to ensure models and/or other solutions are performing as designed.
- T5859 additional Consider energy implications (graphical processing unit, tensor processing unit, etc.) when designing AI solutions.
- T5870 additional Design and develop continuous integration/continuous delivery (CI/CD) in a containerized or other reproducible computing environment to support the machine learning life cycle.
- T5871 additional Design and develop machine learning models to achieve organizational objectives.
- T5872 additional Design, develop, and implement AI tools and techniques to achieve organizational objectives.
- T5873 additional 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.
- T5889 additional Identify and submit exemplary AI use cases, best practices, failure modes, and risk mitigation strategies, including after-action reports.
- T5893 additional 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.
- T5896 additional Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.
- T5915 additional Research the latest machine learning and AI tools, techniques, and best practices.
- T5925 additional Use knowledge of business processes to create or recommend AI solutions.
- T5926 additional Use models and other methods for evaluating AI performance.
- T5927 additional Write and document reproducible code.
- T764 additional Perform secure programming and identify potential flaws in codes to mitigate vulnerabilities.
Knowledge, Skills, and Abilities
KSA statements define what a person filling this role knows or can do. "Knowledge" is what they must know, "Skill" is what they can perform, and "Ability" is a durable capacity they bring to the work.
- A6060 ability core Ability to collect, verify, and validate test data.
- K0021 knowledge core Knowledge of computer algorithms.
- K0102 knowledge core Knowledge of programming language structures and logic.
- K075A knowledge core Knowledge of mathematics, including logarithms, trigonometry, linear algebra, calculus, statistics, and operational analysis.
- K6311 knowledge core Knowledge of machine learning theory and principles.
- K7009 knowledge core Knowledge of coding and scripting in languages that support AI development and use.
- K7011 knowledge core Knowledge of current AI and machine learning systems design and performance analysis models, algorithms, and tools.
- K7020 knowledge core Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).
- K7024 knowledge core Knowledge of how AI is developed and operated.
- K7028 knowledge core Knowledge of how to automate development, testing, security, and deployment of AI/machine learning-enabled software.
- K7029 knowledge core Knowledge of how to collect, store, and monitor data.
- K7031 knowledge core Knowledge of how to structure and display data.
- K7032 knowledge core Knowledge of how to use data to tell a story.
- K7037 knowledge core Knowledge of machine learning operations (MLOps) processes and best practices.
- K7038 knowledge core Knowledge of metrics to evaluate the effectiveness of machine learning models.
- K7045 knowledge core Knowledge of the AI lifecycle.
- K7046 knowledge core Knowledge of the basic requirements for the successful delivery of AI solutions.
- K7048 knowledge core Knowledge of the benefits and limitations of AI capabilities.
- K7049 knowledge core Knowledge of the latest machine learning and AI tools, techniques, and best practices.
- K7050 knowledge core Knowledge of the nature and function of technology platforms and tools used to create and employ AI.
- K7051 knowledge core Knowledge of the possible impacts of machine learning blind spots and edge cases.
- S166 skill core Skill in conducting queries and developing algorithms to analyze data structures.
- S6760 skill core Skill in writing scripts using R, Python, PIG, HIVE, SQL, etc.
- S7055 skill core Skill in analyzing the output from machine learning models.
- S7057 skill core Skill in building and deploying machine learning models.
- S7059 skill core Skill in creating machine learning models.
- S7065 skill core Skill in explaining AI concepts and terminology.
- S7067 skill core Skill in identifying low-probability, high-impact risks in machine learning training data sets.
- S7075 skill core Skill in testing and evaluating machine learning algorithms and/or AI solutions.
- K0942 knowledge additional Knowledge of the organization's core business/mission processes.
- K6290 knowledge additional Knowledge of how to leverage government research and development centers, think tanks, academic research, and industry systems.
- K7003 knowledge additional Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation solutions.
- K7021 knowledge additional Knowledge of emerging trends and future use cases of AI.
- K7022 knowledge additional Knowledge of how AI adoption can assist developers with service-oriented design.
- K7025 knowledge additional Knowledge of how AI solutions integrate with cloud or other IT infrastructure.
- K7026 knowledge additional Knowledge of how commercial and federal solutions solve Defense-related data environment and platform challenges.
- K7036 knowledge additional Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.
- K7040 knowledge additional Knowledge of Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability considerations for AI solutions.
- K7041 knowledge additional Knowledge of remedies against unintended bias in AI solutions.
- K7044 knowledge additional Knowledge of testing, evaluation, validation, and verification (T&E V&V) tools and procedures to ensure systems are working as intended.
- S7069 skill additional Skill in identifying risk over the lifespan of an AI solution.
- S7071 skill additional Skill in labeling data to make it more discoverable and understandable.