Data Scientist

AeroVironment•Deale, MD

2d•$107,000 - $212,000

About The Position

AeroVironment is seeking a Data Scientist or well-experienced Data Analyst to support mission-focused data analysis and deliver actionable insights from complex datasets. The role applies data science and analytical methods to mission-specific needs, including geospatial/GIS, time-series, and natural language processing data, and uses large language models for analytical and mission-related applications. The position requires an active TS/SCI security clearance and access to information regulated by ITAR and/or EAR; applicants must provide documentation verifying U.S. person status, and some positions may require current U.S. citizenship due to contract requirements.

Requirements

Bachelor’s degree in Data Science, Mathematics, Statistics, Computer Science, Engineering, or a related field.
At least 4 years of experience as a Data Scientist, Data Analyst, or in a related analytical role.
Experience in one or more of the following: geospatial/GIS, time-series analysis, natural language processing, or use of large language models (excluding LLM creation or training).
Programming experience with Python, Bash, and SQL.
Experience using Jupyter Notebook/Lab.
Experience with Python libraries such as Pandas, Geopandas, NumPy, PySpark, SciPy, Plotly, and Plotly Dash.
Experience with ETL, data cleaning, data management, data analytics, and data visualization.
Active TS/SCI security clearance.
Experience with problem decomposition and analytical solution development.
Strong problem-solving skills.
Ability to work independently.
Strong written and verbal communication skills.
Ability to provide documentation verifying U.S. person status under ITAR and EAR; some positions require current U.S. citizenship due to contract requirements.

Responsibilities

Build and assess analytic models tailored to mission-specific needs.
Apply data science and analytical techniques to geospatial/GIS, time-series, and natural language processing datasets.
Communicate principal conclusions clearly using mathematics, statistics, and computer science methods.
Develop reproducible workflows and ensure data integrity.
Develop qualitative and quantitative methods for exploring and assessing datasets in varying states of organization and cleanliness.
Organize, clean, and curate large datasets for analysis.
Present complex technical findings accessibly to technical and non-technical stakeholders.