Data Scientist

Peraton

. Lead end-to-end analytical solution development, from data exploration through model deployment and performance optimization .

Posted 10/6/2026full-timeUnited StatesMid-LevelSenior💰 $104,000 - $166,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying advanced statistical and machine learning models, with a strong focus on feature engineering, data processing workflows, and analytical visualizations. Proficient in Python, SQL, and data science libraries, with the ability to communicate complex insights to diverse stakeholders.

Highest-signal resume keywords

Python ProficiencySQL SkillsMachine Learning Model DevelopmentData Visualization SkillsCloud Environment Familiarity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical AnalysisSupervised LearningUnsupervised LearningFeature EngineeringETL/ELT LogicData Pipeline DevelopmentModel EvaluationCI/CD ConceptsDeep Learning FrameworksGraph Databases

Soft Skills

Excellent Communication SkillsStakeholder EngagementProblem-Solving Ability

Tools & Technologies

PandasNumPySciKit-LearnMatplotlibSeabornAWSAzureDatabricksDockerAmazon SageMaker

Certifications & Qualifications

Public Trust Clearance

Industry Keywords

Data ScienceExploratory Data AnalysisModel DeploymentAnalytical VisualizationsKnowledge Graphs

Tech Stack

Tools & technologies

AWSAzureCloudDockerETLNeo4jNumpyPandasPythonPyTorchSparkSQLTensorflow

About the role

Key responsibilities & impact

Lead end-to-end analytical solution development, from data exploration through model deployment and performance optimization
Direct complex exploratory data analysis (EDA), identifying actionable insights, data-quality risks, and opportunities for new features or modeling approaches
Architect and implement advanced statistical and machine learning models, including classical, ensemble-based, and deep learning techniques where appropriate
Design scalable, production-grade feature engineering pipelines and data processing workflows
Develop robust prototype data pipelines and collaborate with engineering teams to harden them for production
Establish modeling best practices, coding standards, validation frameworks, and experiment-tracking methodologies
Evaluate emerging techniques, libraries, platforms, and architectures to enhance model performance and product capabilities
Create analytical visualizations and presentations for technical and non-technical stakeholders, translating complex concepts into actionable recommendations
Work closely with product managers, engineers, and architects to refine POC concepts, define experiment plans, and integrate analytics into broader product designs
Participate in code reviews, knowledge-sharing, and cross-functional collaboration

Requirements

What you’ll need

5 years with BS/BA; 3 years with MS/MA; 0 years with PhD
Proficiency in Python and common data science libraries (Pandas, NumPy, SciKit‑Learn, Matplotlib/Seaborn)
Strong SQL skills and experience with relational or cloud‑based data warehouses
Understanding of statistical analysis, supervised/unsupervised learning, model evaluation, and feature engineering
Experience working with structured and unstructured data, ETL/ELT logic, and basic data-pipeline development
Familiarity with cloud or containerized environments (AWS, Azure, Databricks, or Docker)
Ability to build and iterate on ML pipelines, including versioning, reproducibility, and CI/CD concepts (e.g., MLflow, SageMaker Pipelines)
Skill in producing clear, interpretable, and effective data visualizations
Comfort working through ambiguous or exploratory analytical problems and communicating recommendations clearly
Excellent written and verbal communication skills, including stakeholder-facing presentations
US Citizenship is required
Must have the ability to obtain and maintain a Public Trust clearance
Preferred: Databricks experience (Spark, Delta Lake, MLflow)
Preferred: Snowflake
Preferred: Amazon SageMaker
Preferred: Graph databases (e.g., Neo4j)
Preferred: Deep learning frameworks (PyTorch, TensorFlow)
Preferred: Experience with knowledge graphs or graph-based feature engineering