Data Analysis Post Masters Student

What You Will Do

The Data Analysis Team within the Test Engineering group (V-14) seeks candidates with experience applying data-driven, computational, and analytical techniques to challenges in engineering and the physical sciences. Our mission is to organize and consolidate measured data and employ those data to provide accurate, timely, data‑driven products in support of the annual assessment, stockpile modernization, and other national security missions.

Examples of the work we do include, but are not limited to:

• Utilize efficient data processing and signal analysis techniques on large-scale, multimodal time series data (e.g., vibration, electromagnetic, and environmental) to rapidly detect and characterize signals of interest in support of national security missions, including weapons and nonproliferation efforts.
• Develop and implement statistical methods for engineering and scientific data like functional analysis of variance to provide real‑time decisions with theoretical guarantees.
• Apply data science and statistical principles to develop and evaluate a wide range of mathematical models, including generative, regression, and classification approaches in deep learning, as well as linear and other machine‑learning methods.
• Organize, label, and otherwise munge large sets of engineering data for preparation for subsequent analysis.
• Monitor data collections from aircraft performing maneuvers to ensure collected data represent realistic weapons environments using informed engineering knowledge.

Our work integrates diverse theoretical disciplines, including data science, artificial intelligence, classical and Bayesian statistics, and mathematics, and applies them to complex physical systems in areas such as mechanical, electrical, and physical sciences.

We seek candidates whose expertise and interests lie at the intersection of these fields and who are eager to contribute to cross‑disciplinary research and innovation. Depending on the skills and interests of the selected candidate, they may contribute to any of the above example efforts, such as

• Data munging and exploratory analysis
• Implementing existing or developing new mathematical theory for an application
• Contributing to our collaborative code base of analysis tools
• Building advanced artificial intelligence models or pipelines (deep learning, machine learning, custom statistical models)
• Applying foundational engineering knowledge to provide informed decisions to stakeholders

• Experience in both areas:
  • An applied or natural science discipline such as engineering (mechanical, electrical, etc.), physics, or chemistry.
  • A computational or data‑focused discipline such as computer science, data science, applied mathematics, or statistics.
• This combination may be demonstrated through a double major (e.g., Mechanical Engineering and Statistics), a major‑minor pairing, or a major in one field complemented by substantial experience—such as research, projects, or internships—in the other.
• Experience in data‑scientific programming (e.g., MATLAB, Python, Julia, or R).
• Strong interpersonal, oral and written communication skills.
• Demonstrated success working in an integrated team environment.

• Proficiency with object‑oriented programming and version control systems (e.g., Git).
• Experience in data analysis, such as implementing mathematical/statistical models to time series data with fundamental understanding of how the models work.
• Experience in engineering, such as
  • Signal processing, including time and frequency domain analysis.
  • Data acquisition, experimental test setup, and data collection.
  • Modeling and simulation.

• A Master’s degree in engineering, physics, applied mathematics, statistics, data science, or a related technical field earned within the past three years is required. For engineering degrees, graduation from an Accreditation Board for Engineering and Technology (ABET) accredited program is required.
• Must have graduated with a cumulative GPA of 3.2 on a 4.0 scale (or equivalent).

The work location for this position is onsite and located in Los Alamos, NM. All work locations are at the…