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AI Guide for Government

A living and evolving guide to the application of Artificial Intelligence for the U.S. federal government.

Understanding AI job roles and career path

AI practitioner ecosystem

Chapter 2 outlines where AI practitioners should sit within mission areas and program offices. Mission areas should create a space for these emerging data science roles to become part of an Integrated Product Team (IPT) ready to take on AI implementation.

Typically, those roles include the following:

  • Data analyst: focuses on answering routine operational questions using well-established data analysis techniques, including AI tools.
  • Data engineer: focuses on carefully building and engineering data science and AI tools for reliability, accuracy, and scale.
  • Data scientist: focuses on thoughtfully and rigorously designing data science/AI models, tools, and techniques. A data scientist should usually have an advanced technical degree and/or significant specialized technical experience.
  • Technical program manager: manages software development teams, including teams building AI tools and capabilities. The job responsibilities of the role are nontechnical, as with all management roles, but a technical background greatly enhances this particular type of manager’s effectiveness.

However, AI practitioners are not only doing technical work. When agencies are planning AI projects, it’s important to narrow in on the sponsors and individuals required to execute key project components.

Roles that support data science teams should include:

  • AI champion: Advocates for the AI solution’s value, but ensures the clear, effective, and transparent communication of the AI solution to ensure that it is developed responsibly and produces the intended results.
  • Project sponsor: Identifies and approves opportunities and makes go/no-go decisions. This person coordinates with the AI champion, if they are not the same person, to communicate progress up and down the chain of command.
  • Mission or program office practitioner: Identifies opportunities and provides business and workflow understanding. This person knows the organization’s mission and the day-to-day details of the work performed. This person helps ensure that the AI solution not only performs the task intended, but can also integrate with and the existing program office team.
  • Project manager: Ensures day-to-day progress and communicates with stakeholders and vendors.
  • Business analyst: Provides business, financial, and data understanding.

The roles above may need to liaise among data science, IT, and the mission area’s business needs. The number of most of these roles varies depending on the size of the initiative.

An AI’s project success depends on the makeup of the Integrated Project Team (IPT). Though technical know-how is certainly important, without adequately understanding the challenge you are trying to address and getting buy-in from the mission and program team, the project will fail.

How is this different from any other IT project team?

Due to the iterative, data-dependent nature of AI, misguided or unsupported AI development could have serious consequences down the road.

Career path

While the most common starting point of a data science career is the data analyst role, AI-focused practitioners tend to have more of a computer science background.

They may be more likely to start as a junior data engineer or a junior data scientist. AI practitioners with a pure math background will probably start as a junior data scientist. Data engineering continues to be its own track; otherwise, with more experience and ideally an advanced technical degree, the practitioner becomes a full-fledged data scientist.

Agencies with significant AI implementation talent may also have senior technical positions such as senior data architect or principal data scientist; these expert roles usually indicate extensive technical experience and tend to have decision-making authority on technical matters, and/or advise executives. Some agencies also have academia-like groups dedicated to research and not part of mission or business centers; these groups have positions like research scientist, which tend to require PhDs and very specialized technical knowledge.

AI practitioners may also choose to pursue a management career path, with the most natural transition being from data engineer or data scientist to technical program manager. After that, because data science is embedded in mission and business centers, AI technical program managers are on the same track for higher management positions as all other front-line management positions in mission and business centers.