Lead applied trajectory prediction and aircraft-response modeling for a safety-critical Run-Time Assurance product.
Compensation
Pay within range listed + Bonus + Benefits + Equity
Responsibilities
Lead development, tuning, and validation of TPA and recovery-behavior models using aircraft performance data, command-response assumptions, 3DOF/6DOF flyout concepts, wind effects, uncertainty bounds, and maneuver constraints.
Build Python-based analysis, simulation, HIL, and flight-test workflows to compare predicted versus observed aircraft behavior, identify model gaps, tune parameters, and maintain regression datasets.
Integrate and evaluate trajectory-prediction behavior through off-the-shelf or custom autopilot interfaces, including command modes, vehicle-state inputs, latency, mode transitions, control limits, and telemetry analysis.
Support hardware integration and flight-test campaigns across relevant platforms; help evolve trajectory-prediction and safety behaviors from single-aircraft use cases toward multi-agent collaborative CONOPS.
Produce algorithm handoff artifacts and contribute scoped C/C++ implementation, testing, and debugging as needed.
As a Senior Data Scientist at ServiceNow, you’ll play a highly embedded and influential role within our Data Science organisation, partnering closely with Development and Product teams across multiple products within ITOM.
Responsibilities
Lead the development and evolution of advanced data science and machine learning capabilities, including noise reduction, anomaly detection, root cause analysis, and new AI-powered product features.
Own complex data science problems end-to-end, from problem definition and ideation through experimentation, model development, evaluation, productionization, and deployment.
Develop advanced algorithms and models using techniques such as machine learning, deep learning, NLP, anomaly detection, and statistical modeling.
Partner closely with Product, Engineering, Quality, and Architects to translate product requirements into scalable, reliable ML solutions.
Drive technical direction and mentor other data scientists, contributing to best practices in modeling, experimentation, evaluation, and ML engineering.
Physical scientist/software developer with a background in physics and/or mathematics and strong software coding skills to work onsite at the Stennis Space Center, Mississippi who will contribute to ongoing Navy Research Laboratory (NRL) research and development of operational tools for Navy use.
Compensation
$86,000 - $138,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
Responsibilities
Collaborating with mathematics, physics, and acoustics researchers
Writing code, usually in Python or Matlab, to prototype capabilities
Developing algorithms in collaboration with researchers
Converting prototypes into executable programs in Python, C/C++, or FORTRAN
Interfacing with libraries developed in these and other languages, such as Python or Java
Contribute to solving complex maritime engineering challenges by designing and testing prototype underwater acoustic sensors and communication platforms.
Compensation
$46,500 - $58,000 - $69,500 Annual
Responsibilities
Designing next-generation transducers and acoustic sensors
Rapidly prototyping sensor and communication platforms
Supporting the development of state-of-the-art signal-processing algorithms for underwater sensing and communications
Conducting ocean-acoustic modeling to predict and assess undersea sensor-system performance
Requirements
BS, MS, or PhD in Electrical and Computer Engineering
Develop machine learning models that optimize automation and generate data driven insights. The role involves testing model performance, refining algorithms, and monitoring AI driven systems in production.
Responsibilities
Develop machine learning models to enhance automation and insights.
Test model performance and refine algorithms.
Monitor AI-driven systems in production environments.
Operate independently across multiple work areas.
Lead major technical assignments and supervise teams.
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