Posted by: LMI on Aug 30, 2021
LMI is a consultancy dedicated to powering a future-ready, high-performing government, drawing from expertise in digital and analytic solutions, logistics, and management advisory services. We deliver integrated capabilities that incorporate emerging technologies and are tailored to customers' unique mission needs, backed by objective research and data analysis. Founded in 1961 to help the Department of Defense resolve complex logistics management challenges, LMI continues to enable growth and transformation, enhance operational readiness and resiliency, and ensure mission success for federal civilian and defense agencies.
LMI is seeking an Aircraft Data Analyst/Statistician in the Washington, DC area (open to outside the region if a high amount of desired and required skills can be met). This position requires performing data analysis and building statistical models based on Air Force aircraft utilization, debrief, on/off equipment maintenance, possession, aircraft status (FMC, PMC, NMC), and assignment data in REMIS, IMDS and GO81. Also requires developing random mx actions generation approaches for a simulation and analyzing simulation to ensure the simulation is properly emulating aircraft utilization, statusing, and maintenance actions seen in historical data. Job will require generating input sets for simulation, interpreting results, and communicating findings to technical lead to make refinements to the simulation.
Developing random maintenance actions approaches for the simulation Analyzing Air Force data in IMDS, GO81, and REMIS and describe what can be simulated given the state of the data in these systems Setting up input files to feed random distributions parameters for mx actions into the simulation Transform Air Force Enterprise Data into simulation input files via versions controlled scripts and ETL environments (Dagster, Apache Air Flow, etc..) The ability to read Air Force Technical Data and Technical Orders and use that information to shape maintenance action analysis Work with other analysis and tech lead to analyze simulation results and recommend improvements to simulation Be able to work on "large data sets" using Apache Spark, Dask or other large-scale data processing software Be able to interface with wing and below maintenance organizations to understand what their data means in REMIS, IMDS and GO81
Required Skills Master's degree or higher in aerospace engineering, statistics, operations research, mathematics, or other quantitative discipline At least 5 years' experience in analyzing Air Force data in REMIS This position requires an active DoD Secret Security Clearance Can work in Jupyter Notebooks (via JupyterHub) to write scripts in Python and R and perform analysis Ability to work with large-scale data processing software (Dask, Apache Spark, etc.) The ability to design approaches to generate random variates for thousands of random variables related to aircraft mx actions in the simulation The ability create designs of experiments to test simulation in an efficient manner and perform MANOVA on simulation data The ability to analyze simulation data and recommend improvements to the simulation based on outputs to technical lead and program manager The ability to look at mx actions and determine a status for an aircraft (MC, FMC, NMC, etc.) Strong problem solving and analytical skills The ability to work as part of a teamDesired Skills Performing quantitative analysis at the HAF and/or MAJCOM level in REMIS, GO81, and/or IMDS Familiarity with Air Force supply data systems (ILSS, D200, etc...) and how it interacts with maintenance Familiarity with natural language processing and assess if algorithm is properly deriving the meaning of maintenance actions and discrepancies Familiarity with Dagster or Apache Air Flow for creating ETL workflows Expertise in F-22 and KC-135 data First-hand experience working at an aircraft maintenance squadron or base level maintenance squadron in the Air Force Experience working with Palantir Forge Familiarity with C++, Linux, and the Message Passing Interface (MPI) Familiarity with Make and CMake build tools Familiarity with mathematical optimization techniques (Linear Programming, Mixed-Integer Programing, Goal Programming) Familiarity with git version control system #LI-SH1
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