Data Scientist, Mid

Booz Allen Hamilton

Confirmed live today Low trust

Quick summary

Work type
On-site
Location
Aurora, CO
Salary
$77,600–$176,000 / yr
Employment
Full-time
Posted
71 days ago
Freshness
Confirmed live today
Closes
Nov 14, 2026
Nearby
99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $162k
This role $127k
$62k $225k
below market most similar roles pay here above market

This role pays less than 67% of similar roles. Most pay $126,800–$196,250 — the blue band above. At the midpoint, this role pays about $127k versus about $162k for comparable roles.

Based on 240 similar postings.

Employer

About Booz Allen Hamilton

Booz Allen Hamilton is a management and technology consulting firm that provides analytics, digital, engineering, and cybersecurity solutions primarily to U.S. government agencies and commercial clients. Industry: Management & Technology Consulting

Booz Allen Hamilton currently has 1662 open roles on FindRole.

Listed pay typically runs $86,800–$198,000 across 904 roles with salary data.

Most-posted roles

View all roles at Booz Allen Hamilton

At a glance

TL;DR · Data Scientist, Mid

Role Posting Log in to save

JOB TITLE: Data Scientist, Mid The Data Scientist, Mid joins a team focused on transforming complex structured and unstructured data into objective answers to solve global challenges. You will work closely with clients to understand their needs, digging into data-rich environments to develop algorithms and systems. Your daily responsibilities include building advanced analytics and AI/ML models, performing data retrieval, cleaning, modeling, and accuracy testing. You will utilize tools and frameworks including R, Python, SQL/NoSQL, and distributed computing technologies like Hadoop, Spark, and Kafka. Key skills involve mathematics, statistics, probability modeling, and data mining. You must be able to visualize complex concepts for non-technical leadership. This role supports DoW or IC clients on classified networks, addressing problems in areas such as fraud detection, cancer research, and national intelligence.

What does a Data Scientist earn?

Median $173200 from 338 postings across 66 companies.

See salary data

Skills

Machine Learning Artificial Intelligence LLMs Agentic AI Python R SQL NoSQL Spark Hadoop Kafka Hive EMR MapReduce Plotly Seaborn ggplot2 Gurobi MySQL Data Engineering Data Warehousing NLP Statistics Probability Modeling Data Mining

What you'll do

  • Develop algorithms and systems to turn disparate data points into objective answers.
  • Build advanced analytics and AI/ML models, including tools leveraging LLMs or agentic AI.
  • Perform data retrieval, cleaning, modeling, and accuracy evaluation/testing.
  • Translate complex technical findings into graphical, written, visual, and verbal narrative products.
  • Visualize complex data and concepts for non-technical leadership to highlight system risks.
  • Articulate "go or no-go" criteria clearly to help clients make informed decisions.
  • Provide a deep understanding of data meaning and practical applications to clients.

What we're looking for

  • 3+ years of experience correlating complex technical findings into graphical, written, visual, and verbal narrative products.
  • Experience building advanced analytics and AI/ML models, including LLMs, agentic AI, data retrieval, cleaning, modeling, and accuracy testing.
  • Experience working on classified networks in support of DoW or IC clients.
  • Ability to visualize complex data for non-technical leadership, highlighting system risks and articulating "go or no-go" criteria.
  • Active TS/SCI clearance and willingness to take a polygraph exam.
  • Bachelor’s degree and 3+ years of experience with scientific techniques like mathematics, statistics, AI, machine learning, or data engineering.
  • 7+ years of experience with scientific techniques like mathematics, statistics, AI, machine learning, or data engineering in lieu of a degree.
  • 2+ years of experience in algorithm development using R, Python, or SQL/NoSQL (preferred).

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