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Altamira Technologies has a long and successful history providing innovative solutions throughout the U.S. National Security community. Headquartered in McLean, Virginia, Altamira serves the defense, intelligence, and homeland security communities worldwide by focusing on creating innovative solutions leveraging common standards in architecture, data and security. Altamira believes that our people and the culture of our company differentiate us from other companies.

Altamira Technologies is seeking a Senior Applied Mathematician to support implementation of statistical learning and machine learning approaches, radar and electro-optical data processing, and probability theory research for U.S. Air Force and Intelligence Community missions. The successful candidate will apply advanced probability, stochastic processes, statistical inference, machine learning, and statistical learning to noisy and sparse mission data, translating complex mathematical concepts into practical algorithms and analytic tools.

This position will work closely with mission analysts, engineers, and software developers to improve change detection capabilities, characterize resident space objects, evaluate algorithm performance, and advance statistical methods used in operationally relevant applications.

Responsibilities

• Serve as a subject matter expert in applied mathematics, probability theory, stochastic processes, statistical inference, and event processing.


• Develop and refine probabilistic algorithms, statistical models, and hypothesis tests for feature-based change detection and mission-data analysis.


• Support radar-data processing and change-detection research, development, integration, testing, and evaluation activities.


• Formulate methods for resident space object characterization using noisy, sparse, or incomplete observational data.


• Apply statistical and machine-learning techniques to identify anomalies, characterize changes, and improve analytic performance.


• Develop and assess approaches based on the Sequential Probability Ratio Test (SPRT), statistical model fitting, moving-average methods, clustering, and related techniques, and contribute improvements to mission analytic tools and their underlying logic.


• Design experiments, evaluate algorithm performance, interpret results, and communicate technical findings to government and contractor stakeholders.


• Collaborate with multidisciplinary teams to translate mission needs into mathematically sound and implementable analytic solutions.


• Document methods, assumptions, results, and recommendations in technical reports, briefings, and other program deliverables.

Required Qualifications

• Ph.D. in Statistics, Applied Mathematics, Mathematics, Operations Research, or a closely related quantitative field.


• Ten (10) or more years of professional experience applying advanced statistical or mathematical methods to complex scientific, engineering, defense, intelligence, or national security problems.


• Demonstrated expertise in advanced probability theory, stochastic processes, statistical inference, hypothesis testing, and statistical model development.


• Experience developing probabilistic algorithms or statistical learning approaches for change detection, anomaly detection, object characterization, or similarly complex analytic problems.


• Experience analyzing noisy, sparse, high-dimensional, or otherwise challenging data and explaining complex mathematical concepts to technical and mission-focused audiences.


• Strong written and verbal communication skills, including the ability to prepare technical documentation and present analytic results.


• Ability to satisfy the security and access requirements established for the assigned customer and program.

Preferred Qualifications

• Experience supporting the U.S. Air Force, Department of Defense, Intelligence Community, or other national security customers.


• Experience supporting NASIC missions or working in a NASIC mission environment.


• Knowledge of radar data processing, feature-based change detection, resident space object characterization, or space domain awareness applications.


• Experience with Sequential Probability Ratio Test methods, double exponential moving averages, spline-based statistical modeling, functional data clustering, queueing theory, optimization, or the modeling and evaluation of mission analytic tools.