Data Analyst

Booz Allen Hamilton•Usa, NC

10d

About The Position

As a client-facing Data Analyst, you will guide a small team of analysts, developers, and data consumers in a fast-paced Agile environment. You will use machine learning and statistical modeling expertise to lead the design, development, and deployment of predictive models for clients. You will work with clients to understand their questions and challenges and architect data-driven solutions in data-rich environments. You will build models to predict future outcomes and advise clients on how to interpret and use predictions to proactively shape mission success.

Requirements

Experience developing software or data applications using Python and SQL.
Experience with data visualization techniques and tools such as Power BI or Tableau.
Experience with modern web application technologies, APIs, or full-stack development.
Experience in data validation, feature engineering, and preparing complex datasets for machine learning.
Experience with data modeling, data integration, data transformation, and analytical workflows.
Experience working directly with end users to discover requirements, validate solutions, and drive adoption.
Knowledge of statistical analysis and modeling techniques such as regression, classification, or clustering.
Secret clearance.
High school diploma or GED.

Nice To Haves

Experience with Palantir Foundry and Maven Smart System.
Experience with React, Node.js, Express, FastAPI, MongoDB, Docker, or AWS.
Experience developing custom data-driven applications or user interfaces for data input and visualization.
Experience with Agile methodologies.
Experience developing real-time or event-driven applications.
Ability to communicate technical information and recommendations to peers, engineers, and analytic customers.
TS/SCI clearance.
Bachelor’s degree.

Responsibilities

Guide a small team of analysts, developers, and data consumers.
Lead the design, development, and deployment of predictive models.
Work with clients to understand their questions and challenges.
Architect data-driven solutions within clients’ data-rich environments.
Build models to predict future outcomes.
Advise clients on the meaning and use of model predictions.
Develop software or data applications using Python and SQL.
Validate data, engineer features, and prepare complex datasets for machine learning.
Work directly with end users to discover requirements, validate solutions, and drive adoption.