Data Scientist

Some of the major duties you will perform include but are not limited to the following: Create and refine systems that transfer scientific data, metadata, and related artifacts from internal sources to public repositories and long-term storage; preserve the integrity of and manage access to artifacts.

Deliver data artifacts through user-friendly interfaces and AI-powered apps.

Develop and implement AI and machine learning tools to streamline the release of USGS-approved publications, data, models, and metadata for USGS and public users accessing scientific resources - examples include workflows, forms, editors in backend systems, and chatbots, search interfaces, and other discovery aids in public-facing systems and interfaces.

Use data science and management methods to efficiently maintain scientific data, publications, metadata, and files in SDM-managed repositories and systems, including batch updates, file transfers, storage and access efficiencies, and address changes in federal data and information policies.

Use established and innovative techniques to detect patterns and anomalies in scientific data and metadata, ensuring compliance with Findability, Accessibility, Interoperability, and Reusability (FAIR), and future Federal, Departmental, or Bureau requirements.

Work collaboratively with team members, stakeholders, and subject matter experts to ensure shared understanding of project goals and analytical needs, and employs open communication and coordination skills to help the team align on methods, validate assumptions, and integrate diverse perspectives.

Requirements Continued: Upon completion of your probation your employment will be terminated unless you receive certification, in writing, that your continued employment advances the public interest.

Qualifications: All qualifications must be met by the closing date of this announcement-09/19/2026-unless otherwise stated in this vacancy announcement.

To receive credit for experience, your resume MUST state either "full-time" (or "40 hours a week") or "part-time" with the number of hours worked per week to ensure proper crediting of specialized experience.

Failure to adequately provide information needed to determine number of hours worked in each position may result in that time not being credited when evaluating qualifying experience.

For periods of time that reflect military service, the DD-214 or Statement of Service is sufficient to meet the full and/or part-time hours requirement as the service dates will be reflected.

Minimum Qualification Requirements: To qualify for this position, you must meet the following minimum qualifications: BASIC EDUCATION REQUIREMENT: At all grade levels, applicants must meet the following education requirement to satisfy the basic education requirement for the Data Scientist occupational series.

Possess a Bachelor's or higher degree in mathematics, statistics, computer science, data science or field directly related to the position. The degree must be in a major field of study (at least at the baccalaureate level) that is appropriate for the position.

OR A combination of education and experience with courses equivalent to a major field of study (30 semester hours) as shown in paragraph A above, plus additional education or appropriate experience.?

GS-11: In addition to meeting the Basic Education Requirements, you must have specialized experience, graduate education, or a combination of specialized experience and graduate education as outlined below.

One year of appropriate professional experience that is in or directly related to the duties of the position to be filled is qualifying if it is equivalent to at least the GS-09 level in the Federal service, and if it equipped the applicant with the knowledge, skills and abilities to perform successfully the duties of the position.

Examples of such experience may include: Building, integrating, deploying, and optimizing tools, applications, and capabilities that leverage AI, machine learning, and models.

Applying research computing techniques, including Python and R, and development of Jupyter Notebooks and Pydantic models, for data-intensive analysis, validation, and batch processes.

Applying machine learning and natural language processing techniques, including large language models and retrieval-augmented generation (RAG), to develop tools and automated workflows for chatbot interactions, data validation, content summarization, information extraction, metadata enhancement, and related processes that support data and information management and discovery.

Experience working with Cloud hosting solutions, including AWS or Azure, and file transfer solutions, such as Globus.

Experience using SQL and NoSQL databases, writing queries to extract data and perform update operations. **OR Graduate education: Three full years of progressively higher-level graduate education or Ph.D. or equivalent doctoral degree directly related field of study. **OR Combination: a combination of successfully completed graduate level education, as described above, and professional experience, as described above.(CLICK HERE FOR DETAILS ON HOW TO COMBINE GRADUATE EDUCATION & EXPERIENCE).

Volunteer Experience: 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.