Data Scientist - Supply Chain Risk Management

VTG Defense; Washington (District of Columbia); Hybrid; USD 90,000 - 120,000; Full time

Job summary

VTG Defense is seeking a motivated Data Scientist to join their team in Washington, DC, supporting Supply Chain Risk Management. This hybrid role requires applicants to apply statistical methods and machine learning to create data-driven insights.

The successful candidate will also be responsible for analyzing complex datasets, using Python, and collaborating with cross-functional teams. A bachelor’s degree in a relevant field and U.S. citizenship are required. Pay is between $90,000 and $120,000 annually.

Qualifications

• 1-3 years of hands-on experience in data science, analytics, or applied machine learning.
• Strong proficiency in core data science libraries in Python.
• Must have and maintain a Secret clearance, eligible for Top Secret clearance.

Responsibilities

• Apply statistical methods and machine learning to identify risks in supply chains.
• Write clean, efficient code using Python and data science libraries.
• Analyze structured and unstructured data from various database technologies.

Skills

Statistical methods

Artificial intelligence

Machine learning

Predictive modeling

Python programming

Education

Bachelor's degree in Data Analytics, Statistics, Economics, Supply Chain Management, Computer Science, or related field

Tools

pandas

NumPy

scikit-learn

PyTorch

TensorFlow

SQL

NoSQL

Hadoop

Spark

Hive

Job description

Overview

VTG is seeking a motivated Data Scientist to join our team supporting a program focused on Supply Chain Risk Management (SCRM). The successful candidate will contribute to the development of data-driven insights and analytic solutions that help identify, assess, and mitigate risks in complex supply chains. This position (hybrid role) will report to the Washington Navy Yard in Washington, DC.

What will you do?

• Apply statistical methods, artificial intelligence, machine learning, and predictive modeling to identify patterns, anomalies, and potential risks
• Write clean, efficient, and well-documented code in Python leveraging data science libraries (e.g., pandas, NumPy, scikit-learn, PyTorch, TensorFlow)
• Query, manipulate, and analyze structured and unstructured data from databases (SQL, NoSQL, Hive, etc.)
• Support data ingestion, processing, and visualization workflows to deliver actionable insights to government stakeholders
• Collaborate with a cross-functional team of analysts, data engineers, front-end developers, and subject matter experts to deliver mission-critical solutions
• Present findings through reports, dashboards, and briefings to both technical and non-technical audiences
• Develop and implement data science solutions to analyze large and complex datasets related to supply chain networks

Do you have what it takes?

Required Qualifications:

• Bachelor's degree in Data Analytics, Statistics, Economics, Supply Chain Management, Computer Science, or a related field
• 1-3 years of hands‑on experience in data science, analytics, or applied machine learning
• Strong proficiency in Python and core data science libraries
• Experience with databases (SQL and/or NoSQL) for querying and analysis
• Strong problem‑solving skills and the ability to communicate technical insights clearly
• Must have and maintain a Secret clearance‑eligible for a Top Secret clearance
• U.S. citizenship

Desired Qualifications/Skills/Abilities:

• Background or coursework in Supply Chain Management, Logistics, or Risk Analysis
• Experience with big data tools (Hadoop, Spark, Hive, etc.)
• Familiarity with government contracting or defense‑related projects
• Experience developing in Cloudera Data Science Workbench (CDSW)

Travel Requirements:

Minimal travel, mostly in the Washington DC area.

Position contingent upon funding

Pay Range: VTG's estimated starting pay range is $90,000-$120,000 annually, which is a general guideline for the geographic location. When extending an offer, VTG also considers work experience, education, skill level, market considerations and may possibly include contractual requirements which may cause an offer to fall outside of this range.