Supervisory AI/ML Engineer

Consumer Product Safety Commission•Washington, DC

17d•Onsite

About The Position

The Supervisory AI/ML Engineer, officially titled Supervisory Data Scientist (Artificial Intelligence), is a senior supervisory data scientist at the U.S. Consumer Product Safety Commission (CPSC), in the Directorate for Epidemiology, Office of Risk Reduction. The role leads mission-critical analytics to detect and mitigate consumer product hazards and supports agency policy and enforcement prioritization. The position leads the end-to-end data science lifecycle, from problem framing and experiment design through data curation, model development, validation, interpretability, deployment, and monitoring. It establishes the agency’s research agenda and methodological standards for hazard detection and product risk modeling, and leads analytically intensive programs using advanced statistical modeling and applied machine learning. The work includes building automated, data-driven approaches to evaluate high-volume and high-velocity datasets, and developing solutions using supervised and unsupervised learning, NLP, deep learning, generative pretrained transformers, AI agents, and multiagent orchestration. The incumbent leads a team building AI agents and integrates them with APIs, event streams, dashboards, and case management systems to accelerate triage and decision-making. The role also develops data-fusion products and high-quality pipelines across structured and unstructured sources, with attention to data quality, performance, interoperability, lineage, documentation, and cloud environments such as Azure. It implements model monitoring, alerting, and automated rollback to maintain safe and responsive systems. The position translates complex analyses into decision briefs, dashboards, reports, and clear narratives for technical and nontechnical audiences. It communicates uncertainty and limitations, identifies emerging hazard signals and trends, and uses scenario analysis to help anticipate risks. The incumbent collaborates across the agency, maintains current knowledge of AI/ML developments, participates in relevant working groups and forums, and mentors junior engineers through technical guidance, reviews, and career development. The role also supports adoption and change management while maintaining quality, security, compliance, reproducibility, and governance standards.

Requirements

U.S. citizenship.
Must complete a background investigation and receive a favorable suitability determination; this is a Public Trust position.
Male applicants born after December 31, 1959, must certify Selective Service registration or exemption.
Must complete a Declaration of Federal Employment (OF-306), applicable background forms, and the Personal Identity Verification process.
May be required to serve a one-year supervisory probationary period.
At least 52 weeks of specialized experience equivalent to at least the next lower grade level in the Federal Service.
GS-15 specialized experience must include leading and overseeing enterprise-scale AI/ML and advanced analytics programs; directing the end-to-end data science lifecycle; modernizing cloud-native analytical ecosystems; applying AI/ML to detect risks, hazards, early indicators, signals, or emerging trends; and supervisory or senior technical leadership improving outcomes in complex public-safety or operational environments.
A bachelor’s or higher degree in mathematics, statistics, computer science, data science, or a directly related field; alternatively, 30 semester hours equivalent to a major field of study in one of those areas plus additional education or appropriate experience.
Provide transcripts supporting education claims; unofficial transcripts may be submitted during application, with official transcripts required upon appointment.
Must reside in the local commuting area of the duty station and report to the new Washington, DC location when the headquarters relocation is completed.

Responsibilities

Lead and define the agency’s data science research agenda for hazard detection and product risk modeling.
Establish methodological standards, reproducibility protocols, and peer review processes.
Lead large-scale data science programs using advanced data science methodologies, statistical modeling, and applied machine learning.
Replace manual hazard-detection processes with automated, data-driven approaches for high-volume and high-velocity datasets.
Lead design and implementation of technical solutions including supervised and unsupervised learning, NLP, deep learning, generative pretrained transformers, AI agents, and multiagent orchestration.
Lead a team that builds AI agents and integrate agents with APIs, event streams, dashboards, and case management systems.
Develop data-fusion products that combine data from multiple systems.
Design and implement end-to-end machine learning pipelines, including ingestion, preprocessing, training, evaluation, and deployment.
Implement model monitoring for drift, data and feature quality, bias, and business KPIs, along with alerting and automated rollback.
Design data pipelines across structured and unstructured sources; enforce data contracts, lineage, and documentation.
Partner with analytics teams to make datasets discoverable and performant for iterative model development.
Lead data storytelling through decision briefs, dashboards, reports, and visual analytics for technical and nontechnical audiences.
Identify leading indicators, signals, and trends; perform scenario analysis and what-if simulations to anticipate emerging hazards.
Communicate uncertainty, assumptions, and limitations to senior leaders and agency offices, and connect findings to policy, enforcement, and program impacts.
Maintain current knowledge of AI/ML development and participate in working groups, communities of practice, and agency or governmentwide forums.
Mentor junior AI/ML engineers through coaching, technical reviews, and career-development support.
Provide hands-on supervision in ML pipeline design, coding practices, and responsible AI principles.
Organize knowledge-sharing sessions, pair programming, and code walkthroughs; set expectations and provide actionable feedback.
Support adoption and change management while ensuring quality, security, compliance, documentation, reproducibility, and governance.

Benefits

Paid vacation and sick leave
11 paid holidays per year
Life insurance
Health insurance
Long Term Care insurance
Dental insurance
Vision insurance
Flexible spending account program
Retirement savings and investment plan similar to a 401(k) (Thrift Savings Plan)
Flexible work schedules
Transit subsidies
Fitness center
Employee assistance and wellness programs