Bioinformatics Data Scientist, ForestGEO

Smithsonian Institution; Northern (KY); On-site; USD 85,000 - 91,000; Full time

Benefits offered by this job

Health insurance

Transit/commuter benefits

Job summary

Smithsonian Institution in Washington, DC invites applications for a Data Scientist (Bioinformatics) to support STRI and ForestGEO initiatives. The role emphasizes data management, software development, and analytical data products for tropical forest research.

The position focuses on developing pipelines for ecological data, applying R, Python, SQL, and GIS, and collaborating with site teams to produce reliable data-driven insights for climate and biodiversity studies.

Qualifications

• Professional knowledge of data-science principles, concepts, methods, and practices for ecological data.
• Knowledge of data acquisition, cleaning, validation, transformation, storage, visualization, and reproducible workflows.
• Experience applying programming languages and analytical tools such as R, Python, SQL, Git, GitHub, and GIS to manipulate and manage scientific data.

Responsibilities

• Performs data-management, computational, and bioinformatics tasks supporting tropical biology, ecology, and forestry research.
• Develops data-processing pipelines for plot-based tree inventory and ecological data.
• Evaluates and improves data-processing pipelines based on data characteristics and project needs.
• Writes, tests, and maintains code used in data-processing and analytical workflows.
• Coordinates with researchers to ensure data products meet scientific requirements.

Skills

Data science

Bioinformatics

R

Python

SQL

Git

GIS

Tools

GitHub

GIS software

Job description

Smithsonian Institution in Washington, DC invites applications for a Data Scientist (Bioinformatics) to support STRI and ForestGEO initiatives. The role emphasizes data management, software development, and analytical data products for tropical forest research.

The position focuses on developing pipelines for ecological data, applying R, Python, SQL, and GIS, and collaborating with site teams to produce reliable data-driven insights for climate and biodiversity studies.