Unleash your talents at Leidos! Grow and advance rapidly in your career by letting Leidos challenge you with world toughest technical problems.
Want to be a part of an elite team where our innovative technical solutions are delivered to customers that advance the state of the art while addressing long-term problems of importance to national security? Imagine…this could be you! At our Leidos' Multi-Spectrum Warfare Research and Analytics Systems (MSWRAS) Division, an organization in the Leidos Innovation Center (LInC), we are looking for you, our next Scientist who specializes in remote sensing data analytics. Join our team of Ph.D. level peers in designing and developing advanced technology-based solutions for contract research and development projects working in our Arlington, VA office.
Fun roles you will have in this job:
- Describe instances of successful, proven, and demonstrable experience contributing to the technical work as part of cross-discipline teams in the development and integration of software-based solutions for competitive, contract-based applied research programs
- Work with teams composed of members from industry, small businesses, and academic-based researchers and should have experience working on projects focused on multiple technical fields such as machine learning, artificial intelligence, engineering, and software development and integration
- Describe how the work products to which they contributed had solved customers’ problems in such domains as energy, health, and national security or in the commercial sector
- Work within the MSWRAS Division and across the LInC, performing basic and applied contract research and development projects both leading and working under the guidance of senior scientists and engineers.
- Processing, interpreting and analyzing large volumes of data collected by remote sensing platforms but may also include other types of phenomenological data such as field measurements, or weather data
- Independently design and undertake new research as well as partner in a team environment across organizations
- Contribute to the development of creative and innovative R&D approaches to solving major remote sensing analytics challenges and work with potential sponsors (customers or internal champions) to secure funding for new research efforts based on those topics
- Contribute to the productivity of teams composed of fellow researchers, data scientists, data engineers, and software engineers to execute complex R&D programs
- Under the guidance of a senior scientist or engineer, design and develop or integrate secure and scalable applications that are part of broader solutions, that are applicable across multiple domains.
You will be successful in this role if you have these skills:
- Master’s Degree in engineering, computer science, data science, or a related discipline, such as statistics or applied mathematics 6 years of experience and at least 3 years of specialized experience innovating analytical techniques and performing analytical functions using machine learning libraries or other approaches.
- Must be a US Citizen with the ability to obtain and maintain a Secret clearance and eventually a TS/SCI security clearance.
- Track record of relevant publications in peer-reviewed conferences and journals.
- Experience in one or more subfields of ML or AI: Computer Vision; Pattern Recognition; Predictive Modeling and Decision Support; Reduced Order Modeling.
- Strong data analysis skills and extensive experience with developing and prototyping algorithms in Python and/or Matlab.
- Knowledge of state-of-the-art methods coupled with the creativity and intelligence to advance beyond them.
You will wow us even more if you have these skills:
- TS/SCI clearance
- Familiarity with existing deep learning libraries (e.g., TensorFlow, Spark, Theano, PyTorch, Scikit-learn, Keras, Caffe, Nvidia Digits) and collaboration environments (e.g. Jupyter notebooks, PyCharm)
- Experience with many of the following models: Deep Learning (CNNs, RCNNs, LSTMs), GANs, Autoencoders, Reinforcement Learning, Siamese Networks, Logistic Regression, Linear Regression, Support Vector Machines, Hidden Markov Models, Conditional Random Fields, Latent Dirichlet Allocation
External Referral Eligible
External Referral Bonus:
Potential for Telework:
Clearance Level Required:
Yes, 10% of the time
Scheduled Weekly Hours:
Leidos is a Fortune 500® information technology, engineering, and science solutions and services leader working to solve the world's toughest challenges in the defense, intelligence, homeland security, civil, and health markets. The company's 33,000 employees support vital missions for government and commercial customers. Headquartered in Reston, Virginia, Leidos reported annual revenues of approximately $10.19 billion for the fiscal year ended December 28, 2018. For more information, visit www.Leidos.com.
Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available here.
Leidos will never ask you to provide payment-related information at any part of the employment application process. And Leidos will communicate with you only through emails that are sent from a Leidos.com email address. If you receive an email purporting to be from Leidos that asks for payment-related information or any other personal information, please report the email to firstname.lastname@example.org.
All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.
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