Data Scientist - Junior

We are a world-class team of professionals who deliver next generation technology and products in robotic and autonomous platforms, ground, soldier, and maritime systems in 50+ locations world-wide. Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR).

QinetiQ US's dedicated experts in defense, aerospace, security, and related fields all work together to explore new ways of protecting the American Warfighter, Security Forces, and Allies. Being a part of QinetiQ US means being central to the safety and security of the world around us. Partnering with our customers, we help save lives; reduce risks to society; and maintain the global infrastructure on which we all depend.

Why Join QinetiQ US?

If you have the courage to take on a wide variety of complex challenges, then you will experience a unique working environment where innovative teams blend different perspectives, disciplines, and technologies to discover new ways of solving complex problems. In our diverse and inclusive environment, you can be authentic, feel valued, be respected, and realize your full potential. QinetiQ US will support you with workplace flexibility, a commitment to the health and well-being of you and your family and provide opportunities to work with a purpose. We are committed to supporting your success in both your professional and personal lives.

QinetiQ US seeks an enthusiastic Junior Data Scientist to join a federal law enforcement agency client. This entry-level role provides an excellent opportunity to begin a career in data science while supporting the agency with data analysis and insights.

Working closely with senior team members, you will learn to utilize platforms such as Databricks, Qlik, Tableau, Oracle, ServiceNow, Microsoft Power Suite, Python programming, data visualization, and SQL to support data analysis for the Law Enforcement Lifecycle. This role offers extensive mentorship and training opportunities while contributing to mission-critical federal operations.

Learn to apply basic mathematical methods to analyze datasets using R or Python under senior guidance
• Support data preparation, cleaning, and validation activities for agency systems
• Assist with basic statistical analysis and data exploration tasks
• Learn fundamental concepts of predictive modeling and discrete event simulation (DES)
• Participate in training on multiple correspondence analysis (MCA), principal component analysis (PCA), and association rule mining
• Assist in the professional and technical development of fellow staff

• Gain hands-on experience with platforms including: Databricks, Qlik, Tableau, Oracle, ServiceNow, Microsoft Power Suite, Python, and SQL
• Learn to perform basic queries and data extraction using SQL, MS Access, and MS Excel
• Support senior team members with data processing and analysis tasks
• Participate in training on High-Performance Computing (HPC) environments

• Assist with basic data pipeline activities and data integration tasks
• Learn to work with federal government data from various sources under supervision
• Support data quality checks and validation processes
• Help with extract, transform, load (ETL) operations for law enforcement data

• Learn to create basic reports and visualizations using tools like RMarkdown or Jupyter Notebook
• Assist in preparing data summaries and presentations for team review
• Support documentation of analytical processes and findings
• Practice communicating technical concepts to various audiences

• Participate in team meetings and training sessions
• Assist with project documentation and tracking
• Support ad-hoc data requests and analysis tasks
• Help maintain organized data files and project materials

• Participate in mentorship programs with senior data scientists
• Attend training sessions on federal data requirements and security protocols
• Learn about agency systems and law enforcement operations
• Develop understanding of government data analysis standards and practices

• Bachelor's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or related technical field
• 0-2 years of relevant experience (new graduates welcome)
• Strong academic background in mathematics, statistics, or computer science

• Basic proficiency in R or Python programming
• Fundamental understanding of statistics and data analysis concepts
• Familiarity with SQL and database concepts
• Basic experience with data visualization tools
• Understanding of Excel and basic data manipulation
• Strong analytical and problem-solving skills

• Ability to obtain and maintain appropriate federal security clearance.
• Background investigation required.
• Must be U.S. citizen.

• Coursework or projects involving data analysis, machine learning, or statistics
• Experience with programming languages beyond basic requirements
• Academic projects using real-world datasets
• Understanding of research methodologies

• Familiarity with any business intelligence tools (Tableau, Power BI, etc.)
• Basic understanding of cloud computing platforms
• Exposure to version control systems like GitHub
• Understanding of data privacy and security concepts

• Internships or co-op experience in data analysis or related fields
• Academic research experience involving data
• Participation in data science competitions or projects
• Volunteer work involving data analysis

Accessibility/Accommodation:

If because of a medical condition or disability you need a reasonable accommodation for any part of the employment process, please send an e-mail to staffing@us.QinetiQ.com or call (540) 658-2720 Opt. 4 and let us know the nature of your request and contact information.

QinetiQ US is an Equal Opportunity/Affirmative Action employer. All Qualified Applicants will receive equal consideration for employment without regard to race, age, color, religion, creed, sex, sexual orientation, gender identity, national origin, disability, or protected Veteran status.