Chase your aspirations with no boundaries at Leidos!
Accelerate your career growth by joining our Leidos Innovation Center (LInC) as our latest and greatest Machine Learning (ML) Senior Scientist providing technical leadership and development of innovative solutions in the area of Predictive Maintenance (PM) in Arlington VA. As our ML/PM Senior Scientist you will work with a team focusing on accelerating ML innovation across Leidos by developing enterprise solutions and differentiated technologies, as well as engaging with commercial partners and providing thought leadership. Sounds exciting right!!
As our Senior Scientist you will help develop key capabilities in the area of Predictive Maintenance to support contracts across Leidos in the Defense, Health, Civil, and Intelligence markets, for example for Air Force and Army airframes and vehicles. You will get to define and implement innovative solutions applying ML to the problem of predicting mission readiness and failure rates using disparate data in a streaming data environment. Help us develop concepts for and perform on both internal and contract R&D projects (DARPA, IARPA, AFRL, ARL, and NRO), as well as provide thought leadership internal and external to Leidos in the domain.
The fun stuff you will be doing on the job:
-Develop and communicate innovative solutions applying machine learning to historical platform and maintenance data that use all available data (e.g., operational, environmental) to make actionable and accurate predictions of mission capability and failure rates, for major captures across the enterprise
-Design and develop advanced ML technologies to improve the performance of predictive maintenance solutions and address major and emerging challenges, advancing the state of the art in key areas of Leidos strategic importance
-Communicate and compare challenges, innovations, risks, and impacts of state of the art approaches to predictive maintenance problems in one or more domains within Leidos and LInC
-Collaborate with a team of 25+ Ph. D.-level AI/ML researchers within LInC in different domains to provide differentiated solutions for needs across Leidos
Skills required to be successful in this role:
-BS degree and 12 years of prior relevant experience or Masters with 10 years of prior relevant experience
-Must be able to obtain and maintain a TS/SCI
-Must have experience in developing, training, and evaluating Machine Learning models that use time series data representing maintenance and failure events, applying classification and regression approaches
-Possesses strong understanding and experience in working with incomplete and noisy data
-Experienced in evaluating and selecting features appropriate to Predictive Maintenance modeling, for the purpose of optimizing maintenance actions, minimizing downtime, and finding anomalies
-Experience building and delivering solutions that have delivered operational efficiencies
-Experience with a broad set of commercial and open source data science and Predictive Maintenance platforms
-Hands-on experience with one or more of the following models: Logistic Regression, Linear Regression, Support Vector Machines, and environments (e.g. Jupyter notebooks, PyCharm)
You will wow us even more if you have these skills:
-Ability to develop models leveraging ML libraries using a language/tool such as Python, R Studio
-Experience building and delivering solutions that have delivered operational efficiencies on U.S. Government programs
-Knowledgeable on additional machine learning and deep learning tools (e.g., TensorFlow, Spark, Theano, PyTorch, Scikit-learn, Keras, Caffe, Nvidia Digits)
-Knowledgeable of one or more of the following models: Hidden Markov Models, Conditional Random Fields, Latent Dirichlet Allocation Deep Learning (CNNs, RCNNs, LSTMs), GANs, Autoencoders, Reinforcement Learning, Siamese Networks
-Experience in designing solutions that optimize ML performance through advanced HW and software techniques.
-Doctorate in Computer Science or a related domain with 5 years of prior relevant experience
-TS/SCI preferred. The ability to obtain a TS/SCI clearance is strongly preferred.
External Referral Eligible
External Referral Bonus:Eligible
Potential for Telework:Yes
Clearance Level Required:Top Secret/SCI
Travel:Yes, 10% of the time
Scheduled Weekly Hours:40
Job Family:Research Scientist
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 38,000 employees support vital missions for government and commercial customers. Headquartered in Reston, Va., Leidos reported annual revenues of approximately $11.09 billion for the fiscal year ended January 3, 2020. For more information, visit www.Leidos.com.
Pay and Benefits
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.
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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 [email protected].
Commitment to Diversity
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.