Description
Description
The Customer has an environment dedicated to tradecraft innovation and is a unique addition to the Intelligence Community (IC). This office incorporates new and emerging commercial technologies, along with tailored and specific technical and analytic capabilities in order to achieve its core mission. Partnerships with tradecraft schools and mission elements drive discovery and definition of projects suitable to Customer's capabilities. The Customer provides an environment for concept incubation, prototype evaluation, and pilot execution in the effort to advance technical intelligence, tradecraft, capabilities, and techniques. The efficiencies gained by collocating technical tradecraft capabilities with new employee training and development provides a comprehensive perspective of exactly what new employees may encounter when they go to the field.
TECHNICAL REQUIREMENTS:
Contractor team ensures that all developed applications and modifications to existing Customer applications operate in compliance with applicable documents. Compliance standards could include security accreditation, program management control gates, integration with external services (e.g., access control, auditing), patching and maintaining current versions of software, outage reporting, accessibility requirements, internal-use software reporting, auditing, logging, solution reconstitution testing, and technical directives. Planned activities shall be coordinated with all stakeholders and approved by the Customer. a. The Contractor Team shall, within an Open-source Environment utilizing Artificial Intelligence (AI) or Machine Learning (ML), develop the ability to correlate data from various sources to create real time pattern of life predictability. b. The Contractor Team shall support ongoing development on tools to gather data from various COTS sensor-based sources and develop systems to return data to Customer. c. The Contractor Team shall support transitioning developed tools and systems to Customer's Enterprise system. d. The Contractor Team shall follow policy requirements related to application retooling and development. e. The Contractor Team shall support tool implementation on Customer's Enterprise system. f. The Contractor Team shall modify applications to meet compliance with the Customer's architectural and Authorization and Accreditation (A&A) process. g. The Contractor Team shall develop tools to enable technology starting from various initial maturity levels. h. The Contractor Team shall develop and support scalable and secure functionality into software applications. i. The Contractor Team shall study how to use Machine Learning to simulate human learning activities, to improve their performance by data analyzing without following the instructions of the program. j. The Contractor Team shall develop data framework for data analysis to support end-user data discovery and data correlation. k. The Contractor Team shall design, setup and perform technical demonstrations. l. The Contractor Team shall incorporate multiple technologies for novel solutions. m. The Contractor Team shall enact ad-hoc technical experiments to test tradecraft concepts.
Machine Learning Support (~1 FTE) a. The Contractor shall understand business objectives and develop models that help to achieve them. b. The Contractor shall develop metrics to track progress. c. The Contractor shall understand data structures, data modeling and software architecture. d. The Contractor shall visualize and manipulate big datasets, both structured and unstructured. e. The Contractor shall explore and visualize data to gain an understanding of it, then identify differences in data distribution that could affect performance when deploying the model in the real world. f. The Contractor shall find available datasets that could be used for training machine learning systems. g. The Contractor shall analyze ML algorithms that could be used to solve a given problem and ranks them by their success probability. h. The Contractor shall design and develop machine learning and deep learning systems to automate predictive models. i. The Contractor shall analyze errors of the model and design strategies to overcome them. j. The Contractor shall perform statistical analysis and fine-tunes using test results. k. The Contractor shall verify data quality and ensure it via data cleaning. l. The Contractor shall deploy models to production. m. The Contractor shall spot fraudulent transactions related to ingesting of data or data duplication. n. The Contractor shall define data augmentation pipelines. o. The Contractor shall perform preprocessing or feature engineering on a given dataset. p. The Contractor shall train and retrain models when necessary.
CORE COMPETENCIES:
- Machine Learning Support - Works to find innovative methods to address Customer key intelligence questions using Machine learning. Uses development tools such as Python, leverage statistical reasoning and methods to critically assess datasets and the validity of data science findings. Requires familiarity with intelligence collection approaches, Customers tradecraft, analytic lifecycle, or a demonstrated ability to engage Customer's mission partners to understand and anticipate their priorities.
Qualifications
Required Skills:
TS/SCI with polygraph
1. Demonstrated experience describing complex technical matters to non-technical trained staff.
2. Demonstrated experience in the design, setup and operation of technical demonstrations.
3. Demonstrated experience incorporating multiple technologies for novel solutions.
4. Demonstrated experience enacting ad-hoc technical experiments to test tradecraft concepts.
5. Demonstrated experience with Open source Operating systems (i.e. CentOS Linux, Ubuntu Linux).
6. Demonstrated experience with implementing Docker containers and Vagrant environments to ship and deploy software packages.
7. Demonstrated experience with JavaScript.
8. Demonstrated experience with Java EE.
9. Demonstrated experience with Python.
10. Demonstrated experience with cloud services.
11. Demonstrated experience with Docker.
12. Demonstrated experience with common data science packages, such as Pandas, NumPy, scikit-learn, TensorFlow, Matplotlib, or Seaborn.
13. Demonstrated experience programming in SQL and working with ElasticSearch.
14. Demonstrated experience writing test cases for code.
15. Demonstrated experience using project tracking boards.
16. Demonstrated experience using version control systems like Git.
17. Demonstrated experience with user-interface or user-experience (UI/UX) design.
18. Demonstrated experience engaging with stakeholders and end-users throughout the development process.
SAIC accepts applications on an ongoing basis and there is no deadline.
Covid Policy: SAIC does not require COVID-19 vaccinations or boosters. Customer site vaccination requirements must be followed when work is performed at a customer site.
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