AI Data Analyst

Booz Allen HamiltonArlington, VA
$55,200 - $126,000

About The Position

As an AI Data Analyst supporting expeditionary logistics, you will unlock the information and value held by a data set, machine learning, and artificial intelligence (AI). In an increasingly connected world, massive amounts of structured and unstructured data open new opportunities to logistics. As a data scientist at Booz Allen, you can help turn these complex data sets into useful information to solve global challenges. You’ll work with and learn from your teammates as you develop algorithms and systems. You’ll use the right combination of tools and frameworks to turn sets of disparate data points into objective answers so that your clients can make informed decisions. Ultimately, you’ll provide a deep understanding of the data, what it all means, and how it can be used.

Requirements

  • Experience with common scripting languages such as Python or R
  • Experience with object-oriented programming
  • Knowledge of data processing frameworks such as Pandas, Polars, or Spark
  • Knowledge of structured and unstructured data sources
  • Knowledge of machine learning, AI, or natural language processing
  • Ability to develop predictive data models, quantitative analyses, and visualization of targeted data sources
  • Ability to deploy natural language processing, text mining, or machine learning techniques
  • Ability to obtain a Secret clearance
  • Bachelor’s degree

Nice To Haves

  • Experience in the development of algorithms leveraging R, Python, or SQL and NoSQL
  • Experience with distributed data or computing tools, including MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL
  • Experience working with LLMs, generative AI systems, or agentic frameworks
  • Experience with Maven Smart System
  • Secret clearance

Responsibilities

  • Develop algorithms and systems
  • Use the right combination of tools and frameworks to turn sets of disparate data points into objective answers
  • Provide a deep understanding of the data, what it all means, and how it can be used

Benefits

  • Health benefits
  • Life benefits
  • Disability benefits
  • Financial benefits
  • Retirement benefits
  • Paid leave
  • Professional development
  • Tuition assistance
  • Work-life programs
  • Dependent care
  • Recognition awards program
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