SUPERVISORY AI/ML ENGINEER

Full Job Description

The Supervisory Artificial Intelligence (AI) / Machine Learning (ML) Engineer serves as the agency's senior supervisory data scientist leading mission critical analytics to detect and mitigate consumer product hazards.

This position is located in the Directorate for Epidemiology (EP), Office of Risk Reduction (EXRR) at the U.S. Consumer Product Safety Commission (CPSC).

In addition to the mandatory education requirement, all applicants must have 52 weeks of specialized experience equivalent to at least the next lower grade level in the Federal Service.

Specialized experience is experience that has equipped the candidate with the particular knowledge, skills, and abilities to perform successfully the duties of the position.

Qualifying specialized experience must demonstrate the following: GS-15: 1) Experience leading and overseeing enterprise scale AI/ML and advanced analytics programs; 2) directing the end to end data science lifecycle (data engineering, modeling, validation, deployment, monitoring); 3) modernizing cloud native analytical ecosystems; 4) applying AI/ML to detect risks, hazards, early indicators, signals, or emerging trends; and 5) with supervisory or senior technical leadership improving mission outcomes in complex public safety or operational environments.

Evidence of the above specialized experience must be supported by detailed documentation of duties performed in positions held.

Your resume is the key means we have for evaluating your skills, knowledge, and abilities as they relate to this position.

Therefore, we encourage you to be clear and specific when describing your experience. We will not make assumptions regarding your experience or based on job titles alone.

If your resume does not support your questionnaire answers, we will not allow credit for your response(s).

Experience refers to paid and unpaid experience, including volunteer work done through National Service programs (e.g., Peace Corps, AmeriCorps) and other organizations (e.g., professional; philanthropic; religious; spiritual; community, student, social).

Volunteer work helps build critical competencies, knowledge, and skills and can provide valuable training and experience that translates directly to paid employment.

You will receive credit for all qualifying experience, including volunteer experience. Applicants must meet the qualifications for this position by the closing date of this announcement. Major Duties:

This position is officially titled Supervisory Data Scientist (Artificial Intelligence). The working title is Supervisory AI/ML Engineer.

The Supervisory AI/ML Engineer is directly responsible for the end-to-end data science lifecycle from problem framing and experiments design through data curation, model development, validation, and interpretability, producing statistically rigorous insights that help to shape agency policy and enforcement prioritization and improves detection accuracy.

Work reflects expertise in statistical learning, algorithmic innovation, and causal inference, applying advanced methods to varied structured/unstructured sources at scale.

The incumbent leads a team that builds AI agents that operationalize safety analytics using tools such as Microsoft Copilot Studio, Python agents, and retrieval pipelines to accelerate triage and decision flow.

Integrates agents with APIs, event streams, dashboards, and case management systems to reduce cycle time from signal to action and support proactive hazard detection.

Serves as a technical expert on data science principles and concepts to construct data fusion products that merge data from multiple systems.

Maintains state of the art knowledge of AI/ML development through close contact with industry and other federal activities; participates in AI/ML working groups, communities of practice, and CPSC or governmentwide forums.

The Supervisory AI/ML Engineer: Leads and defines the agency's data science research agenda for hazard detection and product risk modeling; establishes methodological standards, reproducibility protocols, and peer review processes.

Leads large-scale, analytically intensive data science program through advanced data science methodologies, statistical modeling, and applied ML.

Helps to transform hazard detection processes by replacing manual systems with automated data-driven approaches capable of evaluating high-volume, high-velocity datasets.

Leads the design and implementation of advanced technical solutions (e.g.

supervised / unsupervised, Natural Language Processing (NLP), deep learning, agent native and opensource generative pretrained transformers (GPTs), and multiagent orchestration) to extract actionable insights, rigorously evaluate model performance, and enhance product safety decision making.

The role emphasizes data quality, performance, and interoperability in modern cloud environments (Azure), enabling the Commission's strategic acceleration toward a proactive product safety operating model.

Ensures strong cross functional collaboration to advance organizational priorities and deliver the CPSC mission.

Leads data storytelling that transforms complex data and model outputs into clear narratives, decision briefs, and visual analytics (dashboards, reports) for technical and nontechnical audiences; and surfaces foresights by identifying leading indicators, signals, and trends; perform scenario analysis and “what-if” simulations to anticipate emerging hazards and shorten time to intervention.

Communicates uncertainty, assumptions, and limitations to senior leaders and horizontal agency offices; connects insights to current internal and external policy, enforcement, and program impacts; and serves as a architect and implements end-to-end ML pipelines, including data ingestion, preprocessing, model training, evaluation, and deployment.

Implements model monitoring (drift, data/feature quality, bias, and business KPIs), alerting, and automated rollback to keep systems safe and responsive.

Designs high quality data pipelines (ingest, transform, validate) across structured and unstructured sources; enforces data contracts, lineage, and documentation.

Partners with analytics teams to make datasets discoverable and performant for iterative model development.

Mentors and guides junior AI/ML engineers through structured coaching, technical reviews, and career development support.

Provides hands-on supervision in designing ML pipelines, coding best practices, and responsible AI principles.

Fosters a learning culture by organizing knowledge-sharing sessions, pair programming, and code walkthroughs.

Sets clear expectations, provides actionable feedback, and ensures alignment with agency standards for quality, security, and compliance.

Encourages innovation while maintaining rigor in documentation, reproducibility, and governance artifacts. Drives adoption and change management.