Chief Data Scientist
Compensation: $85,413.33 - $135,310.00 /year *
Employment Type: Full-Time
Industry: Information Technology
Chief Data ScientistJob Description:
Leidos' Energy and Environment Division currently has an opening for a Senior Researcher specializing in Machine Learning for our energy research and development business. We are currently supporting the Department of Energy's National Energy Technology Lab (NETL) in the application of Artificial Intelligence/Machine Learning to a number of research efforts that leverage high performance computing and massive datasets in the following domains all relevant to fossil energy:
- Geological and Environmental Sciences
- Materials Engineering and Manufacturing
- Energy Conversion Engineering
- Systems Engineering & Analysis
The position of the Chief Data Scientist/Machine Learning (ML) Senior Researcher is to perform in a demanding, high-energy position requiring flexibility and innovative technical solutions to the challenges of processing, interpreting and analyzing large volumes of data, including text, images and other media. We are seeking individuals with a unique blend of research and operational experience, in order to apply machine learning models and deep learning approaches to the problem of recognition in complex environments given sparse or limited training data. The ML Senior Researcher will provide research direction and project leadership, develop innovative concepts for further exploration, and implement solutions that extend the state of the art for applications that range from automatic image recognition to text classification.
Primary Responsibilities Include:
- Independently design and undertake new research as well as partner in a team environment across organizations.
- Develop topic descriptions for creative and innovative approaches to solving challenges
- Provide leadership & organization to other team members
- Design and implement secure, scalable, and fault-tolerant solutions across a distributed architecture, with the objective of researching and developing machine learning approaches, especially deep learning, applicable across multiple domains.
- Advanced degree in machine learning (Ph.D highly desired) or a related discipline, such as artificial intelligence and/or in key domain areas including Chemical Engineering, Mechanical Engineering, Petroleum Engineering Geosciences, Material Science with a focus and experience with applied machine learning/artificial intelligence.
- At least three years of specialized experience innovating analytical techniques and performing analytical functions using machine-learning libraries and approaches. 8 years of overall experience is preferred. A PhD can count toward 5 years of experience.
- Demonstrated ability to execute pilot projects independently and then guide the implementation of large-scale implementations by subject matter experts.
- Track record of relevant publications in peer-reviewed conferences and journals.
- Knowledge of state-of-the-art methods coupled with the creativity and intelligence to advance beyond them.
- Strong data analysis skills using R or a comparable platform, and one programming language, e.g. Python, Perl, C/C++, Java
External Referral Bonus:
- Undergraduate or graduate degree in Chemical Engineering, Mechanical Engineering, Petroleum Engineering, Geosciences, Material Science
- PhD with publication track record relative to Machine Learning or Artificial Intelligence
- Prior experience as a project lead, preferably as PI
- Familiarity with existing deep learning libraries (e.g., Theano, Caffe, Torch)
- Prior experience with several of the following models and/or methods for supervised, semi-supervised, and unsupervised machine learning: Logistic Regression, Linear Regression, Support Vector Machines, Convolutional Neural Networks, Hidden Markov Models, Conditional Random Fields, Markov Chain Monte Carlo methods, and/or clustering methods such as Latent Dirichlet Allocation
- Experience in GPU development (including GPGPU iOS)
- Ability to obtain a DOE Q Clearance
IneligiblePotential for Telework:
NoClearance Level Required:
Yes, 10% of the timeScheduled 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 .
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 .
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. - provided by Dice
Associated topics: atmospheric, earth science, geology, geomechanics, gis, gravitational, meteorology, oceanographic, petrophysicist, seismology
* The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.
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