Expert Data Analyst

ASRC Federal Mission Solutions is a premier provider of systems engineering, software engineering, system integration and project management services for real-time, mission-critical defense systems. We are currently seeking an Expert Data Analyst to join our team in Moorestown, NJ. Responsibilities: •    Develops, inspects, mines, transforms, and models data to raise productivity, improve decision making, and gain competitive advantage •    Conducts quantitative and qualitative analysis on data to generate insights and information to ensure correct predictive forecasting or classification •    Manages all aspects of end-to-end data processing utilizing customized report building functions of systems •    Maintains analytical systems, verifies the accuracy of the data, and acts as liaison with business•    Implementing emerging AI/ML technologies from commercial and academic domains to support military and government agencies with prediction, classification, and anomaly detection tasks•    Evaluating, visualizing, and performing statistical analysis on data sets including time-series, multi-modal, and textual data•    Developing appropriate model architectures, custom loss functions, and training deep neural networks including MLPs, CNNs, Transformers, and others as required•    Working with existing modeling and simulation (M&S) tools to develop data for new AI/ML models •    Recommends operational plans and strategies that will directly impact the achievement of overall functional results  •    Serves as a Technical lead on large, complex projects •    Impacts functional strategy by developing new solutions, process, standards •    Responsible for improving upon existing processes and systems using significant conceptualizing, reasoning and interpretation  •    Responsible for conducting briefings to senior leaders both within and outside of the job function•    Responsible for providing guidance, coaching and training to other employees across ASRC Federal within technical area of expertise •    Responsible for leading large, complex project initiatives of strategic importance to the organization, involving large cross-functional teams (without direct reporting relationships) Basic Qualifications:•    Bachelor's degree and 12-15 years of experience or equivalent•    This position requires the ability to obtain and maintain a government clearance, U.S. Citizenship is required•    This position requires the successful applicant to obtain and maintain the required security clearance or other authorization(s) within the necessary timeframe required by applicable contract(s)•    Expert level knowledge of machine learning techniques including popular frameworks (e.g., PyTorch, TensorFlow)•    Familiarity with training pipelines such as RayTune, MLFlow, etc.•    Extensive experience with LLM prompting and Transformer architecture•    Experience with unsupervised learning including clustering algorithms and manifold learning.•    Requires mastery of knowledge within multiple job areas typically obtained through advanced education combined with experience •    Viewed as an expert and/or resource within the field by peers within and outside ASRC Federal •    Requires sound business acumen and combines this with technical knowledge for holistic solutions•    Establishes operational plans for job area with short- to mid-term impact on results (e.g., 1-2 years) •    Demonstrates leadership in technical innovation for ASRC Federal and Operating Groups in support of strategic goals and mission •    Proven ability to utilize extensive analysis to seek understanding problemsProven ability to Communicate •    Ability to influence others to accept job function’s view/practices and agree/accept new concepts, practices, and approaches •    Requires ability to communicate and influence senior executive leadership regarding matters of strategic importance to the organization  Preferred Qualifications: •    Experience with military simulators (e.g., AFSIM, JGTS, MAST)•    Strong writing and presentation skills are preferred•    Academic publication history