Data Scientist

Peraton

3d•$104,000 - $166,000

About The Position

Peraton is seeking a Data Scientist to support the design, development, and deployment of analytical and machine learning components used in customer‑facing products and internal proofs of concept (POCs). This role is ideal for someone who can operate independently, learn quickly, and contribute to a fast‑paced, exploratory development environment. In this role, you will: 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 compelling 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

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.
Comfortable 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.

Nice To Haves

Databricks experience (Spark, Delta Lake, MLflow)
Snowflake
Amazon SageMaker
Graph databases (e.g., Neo4j)
Deep learning frameworks (PyTorch, TensorFlow)
Experience with knowledge graphs or graph‑based feature engineering

Responsibilities

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 compelling 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