Climate Intelligence Scientist – Booz Allen Hamilton
Location: Washington, District of Columbia, USA
Remote Work: Yes
Job Number: R0147647
Are you an advanced scientist and researcher who wants to be at the cutting-edge of data science and artificial intelligence? If you care about moving climate mission forward as much as advancing the field of data science, this is the opportunity for you. Your deep data assimilation expertise and consulting mindset, coupled with an original approach to your work, will help clients and stakeholders make sense of their data and enable actionable results. We’re looking for someone like you to lead complex data exploration and analytics projects through your experience with quantitative and modeling skills to transform signals into insights, and insights into actions. You should have strong data assimilation skills, analytical insights, excellent communication abilities, and a knack for working across teams in a fast-paced environment.
As a climate intelligence scientist on our team, you’ll apply innovative machine learning and atmospheric data assimilation approaches to evaluate and analyze large datasets to improve model performance, for deterministic or probabilistic predictions. You’ll grow your skills in data assimilation, AI/ML, and modeling prediction analysis and shape the future of analytics through a variety of means like prototyping new systems and solutioning for bids and proposals.
We’ll keep you sharp and moving forward in your career with access to top industry professionals and latest AI/ML tools via our partnerships and training opportunities. You will play an integral role in advancing the development of climate intelligence solutions through applying data assimilation and machine learning methods to solve large scientific challenges faced by our country’s federal agencies. This position is open to remote delivery anywhere within the U.S., to include the District of Columbia.
Join us. The world can’t wait.
- Experience with machine learning and atmospheric data assimilation, including evaluating and analyzing large datasets to improve model performance for deterministic or probabilistic predictions
- Experience with large scale computational implementations for scientific applications
- Experience with ensemble data assimilation and Kalman filtering techniques
- Experience with object-oriented design principles and high-level languages, including C++ or Java
- Knowledge of high-performance computing environments and scripting languages, including Python, R, Xarray, pandas, or MATLAB
- Knowledge of Atmospheric, Ocean, or Earth system modeling
- Knowledge of ML techniques, analyses, use of new datasets, new conceptual ideas or new workflow recommendations
- Ability to obtain a security clearance
- Master’s degree
Nice If You Have:
- Experience in model development in various infrastructures, including the Earth System Modeling Framework (ESMF) and NOAA Environmental Modeling System (NEMS)
- Experience with modern software engineering practices, including requirements gathering, design, prototyping, version control, integration, testing and documentation
- Experience working in the public sector
- Experience running advanced Numerical Weather Prediction (NWP) models
- Knowledge of federal acquisition cycle
- Knowledge of the physical and mathematical basis of geophysical modeling, including oceanic and atmospheric
- Doctorate degree
Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information.
The proposed salary range for this position in Colorado is 130,000 to 160,000. Final salary will be determined based on various factors.
At Booz Allen, we celebrate your contributions, provide you with opportunities and choice, and support your total well-being. Our comprehensive benefit offerings include healthcare, retirement plan, insurance programs, commuter program, employee assistance program, paid and unpaid leave programs, education assistance, and childcare benefits.
Build Your Career:
At Booz Allen, we know the power of analytics, and we’re dedicated to helping you grow as a data analysis professional. When you join Booz Allen, you’ll have the chance to:
- change the world with the Data Science Bowl—the world’s premier data science for social good event
- participate in partnerships with data science leaders, like our partnership with NVIDIA to deliver Deep Learning Institute (DLI) training to the federal government
- access online and onsite training in data analysis and presentation methodologies and tools like Hortonworks, Docker, Tableau, and Splunk
You’ll have access to a wealth of training resources through our Analytics University, an online learning portal specifically geared towards data science and analytics skills, where you can access more than 5000 functional and technical courses, certifications, and books. Build your technical skills through hands-on training on the latest tools and state-of-the-art tech from our in-house experts. Pursuing certifications that directly impact your role? You may be able to take advantage of our tuition assistance, on-site bootcamps, certification training, academic programs, vendor relationships, and a network of professionals who can give you helpful tips. We’ll help you develop the career you want as you chart your own course for success.
We’re an equal employment opportunity/affirmative action employer that empowers our people to fearlessly drive change – no matter their race, color, ethnicity, religion, sex (including pregnancy, childbirth, lactation, or related medical conditions), national origin, ancestry, age, marital status, sexual orientation, gender identity and expression, disability, veteran status, military or uniformed service member status, genetic information, or any other status protected by applicable federal, state, local, or international law.
Learn more: https://careers.boozallen.com/jobs/JobDetail/Washington-Climate-Intelligence-Scientist-Senior-R0147647/63572
With the following application instructions:
To apply, applicants can send their resume to Grace Kim (email@example.com) for an internal referral, or submit online directly.
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