Artificial Intelligence / Machine Learning (AI/ML) Specialist (Job Number:433222)
The Machine Learning Specialist will provide direct support to the United States Air Force Shadow Operation Center, Nellis Air Force Base (AFB), Nevada.
- The candidate will leverage knowledge of data science, methodologies, and processing techniques to analyze vast amounts of sensor data for decision support.
- The candidate will work on a small agile team to develop machine learning analytics across domains.
- The candidate will possess knowledge in Machine Learning, to include: statistical analysis, data mining, temporal and pattern analysis, correlation of events, predictive modeling, and pattern recognition.
- The candidate will employ programming skills to perform analysis.
- The candidate will document and visualize analytics both temporally and spatially, and present analytic results and uncertainty to decision makers.
- Ad-hoc informational briefings may also be required to explain methodologies and analytical findings to peers and customer stakeholders.
- The candidate will also investigate and implement new scientific analysis and methodologies to support big data analytics efforts and stay current with evolving technologies and capabilities.
TYPICAL EDUCATION AND EXPERIENCE:
- Bachelors and nine (9) years or more experience; Masters and seven (7) years or more experience ; PhD or JD and four (4) years or more experience.
- Must hold an active Top Secret Security Clearance (Top Secret SCI eligibility)
REQUIRED EDUCATION AND EXPERIENCE:
- Must have at least 9+ years’ relevant experience in Data Science / Engineering
- BS, Master’s or PhD in data science, math, computer science, physical science, or related field
- 5+ years of machine learning experience.
- Secret clearance with Top Secret SCI eligibility
- Experience with machine learning and artificial intelligence techniques and their implementations in open source technologies.
- Experience in retrieving, manipulating, fusing, and exploiting multiple structured and unstructured data sets from various sources.
- Experience with analyzing large volumes of data using distributed processing architectures (ie. Hadoop) with open source tools (e.g. Spark, Python, or R)
DESIRED EDUCATION AND EXPERIENCE:
- Ability to design analytical lifecycle from data collection to production deployment.
- Ability to assess mission value in a wide range of available data
- Ability to identify problems to which data science can be applied and initiate solutions.
- Ability to identify and analyze anomalous data (including metadata)
- Ability to assess feasibility of existing methods, models and algorithms recognizing the capabilities and limitations of methods.
- Knowledge of the end- to-end data science process, including following agile principles to progress from proof-of-concept analytics through prototypes into production.
- Ability to work in a small mission critical devops team.
- Ability to communicate and present analytics approaches and results Location
- Nevada, Customer Site
SAIC Overview:SAIC is a premier technology integrator providing full life cycle services and solutions in the technical, engineering, intelligence, and enterprise information technology markets. SAIC is Redefining Ingenuity through its deep customer and domain knowledge to enable the delivery of systems engineering and integration offerings for large, complex projects. SAIC has approximately 15,000 employees are driven by integrity and mission focus to serve customers in the U.S. federal government. Headquartered in Reston, Virginia, SAIC has annual revenues of approximately $4.5 billion. For more information, visit saic.com.
EOE AA M/F/Vet/Disability
Job Posting: Jan 5, 2018, 6:43:20 PM
Primary Location: United States-NV-LAS VEGAS
Clearance Level Must Currently Possess: Top Secret
Clearance Level Must Be Able to Obtain: Top Secret/SCI
Potential for Teleworking: No
Travel: Yes, 10% of the time
Shift: Day Job
Nearest Major Market: Las Vegas
Training, Engineer, Secret Clearance, Systems Engineer, Security Clearance, Education, Engineering, Government