Data Scientist

Accelerate progress on JADC2 related strategies for the Combatant Command (CCMD) in support of the AI and Data Acceleration. Support the integration and scaling ongoing and proven capabilities used in real-world operations, simulations, experiments, and demonstrations. Improve the organization’s data management with CDAO, ADVANA, CDO, and other internal/external entities to scale existing platforms and assist warfighters in making data visible, accessible, understandable, linked, trustworthy, interoperable, and secure. Expert Python developer using many different machine learning and data science frameworks including TensorFlow, PyTorch, Keras, Ray, RLLib, numpy/scipy, scikit-learn, Caffe, pandas, PyMC3, and numerous others. Utilize knowledge of computer networking concepts and protocols, and network security methodologies to support enterprise data capabilities. Provide risk management matrix (e.g., methods for assessing and mitigating risk) on implementation of data strategies and use-cases in support of critical CCMD missions. Skill in conducting queries and developing algorithms to analyze data structures. Experienced in modern approaches to artificial intelligence, including deep learning, and keep up on latest advances by actively implementing and applying and inferential statistics, sampling, experimental design, parametric and non-parametric tests of difference, ordinary least squares regression, general line). Skill in creating and utilizing mathematical or statistical models. Design, develop, and modify software systems, using scientific analysis and mathematical models to predict and measure outcome and consequences of design. Conduct intelligence activities involving Publicly Available Information and Commercially Available Information according to EO 12333 and DoD 5240.1.R. Skill in developing or recommending analytic approaches or solutions to problems and situations for which information is incomplete or for which no precedent exists. Analyze data sources to provide actionable recommendations. Expert in data enrichment pipelines to support Resource Description Framework (RDF) triplestore, semantic web and graph database. Well-versed in a variety of NoSQL and relational database technologies including Elasticsearch, MongoDB, and modern graph databases such as DGraph and ArangoDB Skill in using deep learning approaches to build machine learning models. Develop data visualizations using tools such as d3.js, bokeh, matplotlib, and others Proficient applying large scale data processing technologies such as the Apache Spark, Kafka, and the Hadoop ecosystem (HDFS, YARN, Zookeeper) to developing scalable advanced analytics including data mining and anomaly detection. Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government. Effective communicator, support technical exchanges with scientists and engineers to expression of high level insights to customers and management Develop, document, implement, and automate ETL (extract transform load) processes using SQL® and other analytical programming tools that most efficiently import structured, semi-structured, and unstructured data from new and/or dynamic data sources, ensuring data validity, reliability, and availability to users. Support implementation of DoD and Intelligence Community Data, Cyber, Artificial Intelligence Strategy. Provide Educational Courses on Data and machine learning to staff, supporting the concept of “Fail Fast, Learn Rapid” in advancing DoD Data Literacy.

Quiet Professionals (dba Endurion) is seeking a dedicated and proactive technical subject matter expert to work in all stages of the data development lifecycle for advanced analytics projects including Big Data, artificial intelligence/machine learning, and other applications to advance current platform capabilities within multiple classification domains. Use data enrichment techniques to feed RDF triplestore and manage data with graph database. Uncovers and explains actionable insights from data by combining scientific method, math and statistics, specialized programming, advanced analytics, AI, and storytelling., Bachelor’s Degree or Higher in one of the following disciplines: Operations Research, Applied Mathematics, Engineering, Science, Computer Science, Mathematics, Statistics or GIS with 3+ years of relevant experience. Experience interacting with “big data” systems such as Microsoft Azure Data Lake, Elastic Cloud, and/or Amazon Web Services (AWS). Experience with various data ingestion techniques and script writing for data ingestion. Proficient in one or more programing languages: R, Python, Java, Scala, HTML, Matlab, R, SQL, C/C++, and JavaScript. Proficient in one or more Libraries: numpy/scipy, scikit-learn, pandas, PyMC3, Ray/RLLib, Theano, TensorFlow, PyTorch, Caffe, Keras, pyspark, OpenCV, AngularJS, D3.js, jQuery, Boost (C++). Proficient in Software/Frameworks: Power Apps, Power Automate, ArcGIS Platform, Docker, Kubernetes, Kibana, Logstash, Node.js, YARN, Zookeeper, HDFS, Apache Spark, Apache Kafka, Ambari, Git, MS Office (Word, Powerpoint, Excel), Unix/Linux, OpenStack, AWS, Azure, VMWare. Proficient in Databases: Dgraph, ArgangodB Elasticsearch, PostgreSQL/PostGIS, MySQL, MongoDB, Redis, Accumulo. Proficient in Geospatial Information Systems (GIS) software (e.g., ESRI ArcGIS® suite) to support data analysis in varying domain classifications. Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity. Knowledge of cybersecurity principles; cyber threats and vulnerabilities; specific operational impacts of cybersecurity lapses. Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS). Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments. Knowledge of statistical/machine learning algorithms. Knowledge of digital rights management. Knowledge of mathematics, including logarithms, trigonometry, linear algebra, calculus, statistics, and operational analysis. Knowledge of programming language structures and logic.

Preferred: Degree in one of the following disciplines: Applied Mathematics, Engineering, Science, Computer Science, Mathematics, Statistics or GIS. Geospatial Information Systems (GIS) software (e.g., ESRI ArcGIS® suite).