Data Scientist - Hybrid

Westat•Bethesda, MD

1d•Hybrid

About The Position

Westat is seeking a Data Scientist to join its Statistics and Data Science team. The role involves programming in R, Python, and SQL; applying data science and artificial intelligence methods—including natural language processing, machine learning, and large language models—to structured and unstructured data; and performing data analytics, visualization, and dashboard development with minimal oversight and supervision. This is a hybrid role working in Westat’s Bethesda, Maryland office two days per week.

Requirements

Ability to obtain a Public Trust clearance, requiring consistent U.S. residency for the majority of the last five years.
Bachelor’s degree in data science, statistics, computer science, mathematics, or another related quantitative field.
At least 4 years of experience in data science or related projects.
Demonstrated experience in R, Python, SQL, and data visualization tools such as Tableau and Power BI.
Demonstrated experience developing machine learning and natural language processing methods.
Experience developing, querying, and managing large data files in databases.
Proven experience using Git as a version control system.

Nice To Haves

Master’s degree in data science, statistics, computer science, mathematics, or another related quantitative field.
Experience working in a professional services research environment.
Knowledge of industry best practices and emerging technologies.
Demonstrated problem-solving abilities and experience developing actionable insights from complex data.
Demonstrated oral and written communication skills for presenting technical concepts to non-technical stakeholders.
Demonstrated organizational skills, including time management, and the ability to handle multiple tasks simultaneously and work independently and in a team environment.

Responsibilities

Develop and test R and Python code to collect, process, link, and analyze data in various formats.
Develop SQL code to query, process, and manage large data files in databases.
Apply supervised and unsupervised machine learning frameworks to data collected from a variety of sources.
Use natural language processing methods and large language models to clean, parse, link, summarize, and analyze large and unstructured data.
Collect or acquire data from sources including websites using APIs and web scraping.
Develop data visualizations and dashboards using open-source libraries in R and Python and proprietary software such as Tableau and Power BI.
Develop and maintain code documentation using Git, implement coding standards, and apply best practices.
Present to internal and external clients.
Engage in thought leadership through publications and conference presentations.