Principal Scientist – AI/ML Specialization

🕒 April 30

⚔️ Virginia – Remote

⚔️ Virginia – Remote

⏰ Full Time

🔴 Lead

🧬 Research Scientist

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Global InfoTek, Inc.

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2001

💼 Consulting

🎖️ Defense

📦 Logistics

Consulting • Defense • Logistics

Global InfoTek, Inc. is a company that specializes in delivering cutting-edge national security solutions with a focus on speed, agility, and innovation. They support federal agencies by providing enterprise application development, cloud engineering, cybersecurity solutions, advanced analytics, and data visualization, as well as artificial intelligence and machine learning. Their mission is to equip the national security community with the necessary tools to combat threats and challenges effectively. Global InfoTek is committed to building the careers of top-tier cybersecurity professionals to ensure the nation's security.

📋 Description

• Conduct independent, hands-on data analysis on RF sensor datasets using Python and Jupyter notebooks • Provide technical advice and research direction across a multidisciplinary team • Serve as primary technical advisor to the government sponsor • Design and execute analytical investigations into RF sensor data quality • Produce technical documentation that accurately represents the scope and confidence level of analytical results

🎯 Requirements

• 10+ years of relevant experience • Advanced degree (MS or PhD) in Electrical Engineering, Computer Science, Applied Mathematics, or a closely related quantitative field • Proven ability to quickly acquire domain knowledge • Demonstrated ability to independently develop and execute data analyses in Python or equivalent tools • Experience addressing common problems with large quantities of real-world data • Track record of working effectively on constrained-hardware edge systems • Deep familiarity with RF signal characteristics, sensor phenomenology, and interpretation of passive receiver data • Hands-on experience applying machine learning to RF or time-series signal data • Familiarity with TDMA network protocols and emitter identification techniques • Experience with interferometric direction-finding and related passive geolocation methods • Background in statistical signal processing and ability to distinguish statistically significant findings from artifacts

🏖️ Benefits

• Health insurance • Paid time off • Professional development opportunities