Data Scientist

ICF

. Use SQL and Python to extract, clean, transform, and analyze structured data from PostgreSQL and other approved sources .

Posted 10/14/2026full-timeRemote • Virginia • United StatesJuniorMid-Level💰 $81,499 - $138,549 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Proficient in SQL and Python for data extraction, cleaning, and analysis, with experience in developing analytical workflows and visualizations using Databricks and AWS QuickSight. Strong understanding of statistics and data governance principles, with the ability to communicate findings effectively.

Highest-signal resume keywords

Python Data AnalysisSQL Data PreparationDatabricks Workflow DevelopmentAWS QuickSight VisualizationStatistical Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLPythonStatistical ModelingData PreparationExploratory Data AnalysisData ValidationData VisualizationMachine LearningCore StatisticsData Governance

Soft Skills

Clear CommunicationCollaborative WorkTask ManagementIncorporating Feedback

Tools & Technologies

PostgreSQLDatabricksAWS QuickSightGitCI/CD

Certifications & Qualifications

Public Trust Clearance

Industry Keywords

Data SciencePublic SectorHealth DataRegulated Data EnvironmentsResponsible AI

Tech Stack

Tools & technologies

AWSCloudNumpyPandasPostgresPySparkPythonScikit-LearnSparkSQLTypeScript

About the role

Key responsibilities & impact

Use SQL and Python to extract, clean, transform, and analyze structured data from PostgreSQL and other approved sources
Perform exploratory data analysis to identify patterns, trends, anomalies, and data-quality issues
Assist with development, testing, and documentation of statistical analyses, machine-learning models, and analytical workflows
Develop and support notebooks and repeatable analytical workflows in Databricks
Create and maintain visualizations, datasets, and reports for DataConnect using AWS QuickSight
Validate analytical outputs and document assumptions, methods, limitations, and results
Partner with data engineers to define data needs, resolve quality issues, and improve analytical datasets
Translate business questions into measurable analyses with guidance from senior team members and stakeholders
Write reusable, version-controlled code and basic unit tests for analytical functions and data transformations
Present findings through written summaries, visualizations, and team demonstrations
Follow standards for data privacy, security, governance, and responsible data use
Participate in Agile planning, refinement, peer reviews, and continuous learning activities
Perform other duties as assigned

Requirements

What you’ll need

Bachelor's degree
2+ years of experience using Python for data analysis, data preparation, and statistical modeling
Candidate must be able to obtain and maintain a Public Trust clearance
Candidate must reside in the United States, be authorized to work in the United States, and have lived in the United States for three full years out of the last five years
Bachelor's degree in data science, statistics, mathematics, computer science, engineering, economics, or a related quantitative field
Academic, internship, project, or professional experience using Python for data analysis
Foundational SQL skills, including joins, aggregations, filtering, and data validation
Understanding of core statistics, data preparation, exploratory analysis, and model-evaluation concepts
Ability to communicate analytical methods and findings clearly in writing and presentations
Ability to work collaboratively, manage assigned tasks, and incorporate feedback
Experience with PostgreSQL or another relational database platform
Experience using Databricks, Spark, notebooks, or cloud-based analytics platforms
Experience creating data visualizations or dashboards with AWS QuickSight or a comparable business-intelligence tool
Experience with common Python data-science libraries such as pandas, NumPy, scikit-learn, or PySpark
Familiarity with TypeScript and its use in data applications, dashboards, services, or integrations
Familiarity with Git, code reviews, unit testing, CI/CD, or Agile development practices
Exposure to data governance, metadata, data quality, privacy, or responsible AI practices
Experience working with health, public-sector, or other regulated data environments