Public Health Data Science Lead

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***This job will close when we have received 150 applications which may be sooner than the closing date.***

• OPEN TO THE PUBLIC

WHAT YOU'LL BE DOING DAY TO DAY

As a Lead Data Scientist, you will:

• Serve as a data science expert in the analysis and classification of public health surveillance, research, and administrative health data; and serve as Team Lead for a team of data scientists.
• Consult and collaborate with statistical, data science, artificial intelligence and public health professionals in the collection, linkage, processing, mining, coding, classification, and analysis of public health surveillance, research, and administrative health data.
• Assist in preparing comprehensive reports that include discussion of substantive issues related to data science methodology for public health surveillance and related applications, assessment of the adequacy and quality of data used in the analyses, and explanation of the methods, results, and relevance to the health issue under study.

Qualifications

WHAT WE ARE LOOKING FOR

Basic Qualifications:

A. Degree: 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

B. Combination of education and experience: Courses equivalent to a major field of study (30 semester hours) as shown in paragraph A above, plus additional education or appropriate experience.

AND

Minimum Qualifications: You must have one year of specialized experience to perform successfully the duties of the position. To be creditable, specialized experience must have been equivalent to at least the GS-13 grade level in the Federal service performing ALL of the following:

• Performing data science work that requires knowledge of public health data systems, converting data, and proficiency data science;
• Developing and analyzing structured and unstructured data; and
• Understanding of data science methods (e.g., machine learning, natural language processing).

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