Mid-Level Data Scientist [$278k/yr+] TS/SCI-FS Poly with Security Clearance
Quick Overview
Job Description
Candidates must already possess an active Top Secret/SCI w/ Full Scope Polygraph to be considered.
Summary: • Develop advanced machine learning models, statistical algorithms, and automated analytics to extract meaningful insights from complex datasets. • Train, evaluate, and tune AI algorithms using rigorous performance metrics to optimize operational workflows. • Build data visualizations and integrate prototype models into production frameworks for end users.
Qualifications && Compensation: • Degree: Technical bachelor's degree or equivalent experience • Years of experience: 7+ years • Total Compensation: $278k+ yearly Job Description: • Build and develop AI models, analyze algorithms and protocols, and tune input and model parameters for reliability and relevance. • Develop machine learning, data mining, statistical, and graph-based algorithms using Python, R, or MATLAB. • Prototype multiple algorithms and determine final models based on rigorous evaluation metrics. • Create models or design experiments to generate synthetic data when training datasets are unavailable. • Design reports and interactive visualizations summarizing datasets to deliver data-driven insights. • Partner with subject matter experts to translate manual workflows into automated analytics. • Implement and transition prototype algorithms into production frameworks for operational integration.
About SYSTOLIC: SYSTOLIC is dedicated to giving our employees the best possible company experience so that they can focus on providing outstanding support to their customer’s mission. Our company is founded on integrity, enthusiasm, and a relentless commitment to supporting the Intelligence Community. You can learn more about us and submit an application to be considered against our current and future openings at https://systolic.com. To learn about our compensation ranges, visit our
Pay Transparency page at: https://systolic.com/pay-transparency
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