Senior Data Scientist

Company Overview

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.

Position Overview

QinetiQ US seeks a highly skilled Senior Data Scientist to support a federal law enforcement agency client. This role is responsible for collecting, analyzing, and interpreting law enforcement data to drive informed operational decisions and improve data quality across agency systems. Your expertise in utilizing Databricks, Oracle (OBIEE/OAS), UiPath, Python programming, SQL, and data visualization will be crucial in identifying data quality issues, developing automated solutions, and providing valuable recommendations to federal stakeholders. 

 

 

Responsibilities

Data Quality Analysis & Engineering

• Apply appropriate mathematical methods to statistically analyze agency data quality issues using Python, R, and SQL
• Create tools for identifying, monitoring, and implementing data quality solutions utilizing Databricks, Oracle (OBIEE/OAS), UiPath, VBA, Python, R, and SQL
• Develop algorithms for automated data quality error remediation and validation processes
• Ensure accuracy of data uploaded to agency systems

Advanced Analytics & Automation

• Conceptualize, plan, design, and develop machine learning algorithms for data quality optimization and automated error detection
• Implement predictive models to identify potential data quality issues before they impact operations
• Perform multiple correspondence analysis (MCA), principal component analysis (PCA), and association rule mining on law enforcement datasets
• Develop automation solutions using emerging tools including UiPath for repetitive data quality tasks

Reporting & Communication

• Produce executive-level summary reports and briefings of data quality analysis results using RMarkdown, Jupyter Notebook, or equivalent tools
• Create comprehensive audit reports for agency Dashboards ensuring accuracy, completeness, and usability standards
• Develop data visualizations and dashboards for federal stakeholders to monitor data quality metrics
• Present complex technical findings to federal staff and law enforcement personnel in accessible language

Technical Support & Collaboration

• Provide ad hoc after-hours support for emergency taskings requiring advanced analytics
• Collaborate with federal staff to identify and resolve historical data errors in law enforcement systems
• Support development of training materials and system documentation for data quality processes
• Serve as technical subject matter expert for cross-unit data quality initiatives
• Assist in the professional and technical development of fellow staff

Required Qualifications

Education & Experience:

• Bachelor's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Information Systems, or related technical field
• Minimum 10 years of relevant data science and analytics experience

Technical Requirements:

• Demonstrated proficiency in R and Python, and familiarity with other leading statistical and data analytical software and frameworks, including Databricks and Qlik
• Proven ability to produce executive level summary reports/briefings using RMarkdow