Affordability Analyst/Data Scientist

📁
Artificial Intelligence/Machine Learning/Data Science
💼
NSAD - National Security Analysis Department

Are you seeking an opportunity to integrate affordability into the design of systems?  Do you enjoy the challenge of interpreting and organizing data into a structured analytic framework?  If so, we’re looking for someone like you to join our team at APL.

 

We are seeking a data scientist to help us develop robust cost estimates and perform timely affordability analysis in support of DoD / US Govt. Sponsors.  Significant tasks will include the collection, normalization and relation of cost and parametric data in order to generate credible and defendable cost estimates.  You’ll be joining a hardworking team of analysts who incorporate affordability and cost considerations into the critical solutions to the critical challenges our nation faces.  As a member of our team, you will contribute to important trade space studies (Analysis of Alternatives, Business Case Analyses), Cost and Schedule Risk Assessments, and affordability studies (Life Cycle Cost Estimates, Total Ownership Costs).



As an Affordability Analyst/Data Scientist...

  • Your primary responsibility will be aggregating cost and parametric data of government projects through both structured (DAMIR, CADE, VAMOSC, USASpending.gov) and unstructured (open source web searches, domain expert interviews) research in order to improve upon existing or generate new and innovative cost estimating relationships.

  • You will contribute to studies that quantify best value assessments through affordability analysis, decision frameworks, data visualization, feasibility analysis, and sensitivity analysis.

  • You will learn to build cost estimates integrating statistical analysis, modeling and simulation, cost databases, and expert opinion in Excel, ACEIT, Price Trueplanning, etc.

  • You will collaborate with APL staff from all sectors and departments on technical studies across land, sea, air, space, and cyberspace domains.

 

You meet our minimum qualifications for the job if you...

  • Possess a bachelor’s degree or equivalent years of professional level experience
  • Proven experience organizing data into flexible, scalable, and reusable structures and performing comprehensive data analysis.
  • Have knowledge of at least one statistical analysis package (R, Stata, SAS, JMP, Python, MATLAB, CO$TAT, @RISK, etc.)
  • Are able to obtain an Interim Secret level security clearance by your start date and can ultimately obtain a Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.


You’ll go above and beyond our minimum requirements if you…
  • Have domain expertise in creating data analysis dashboards through Shiny, Python, JavaScript, CSS, Visual Basic, Tableau, etc.
  • Have a master’s degree or higher in operations research, statistics, mathematics, computer science, engineering, data science, business analytics, cost estimating or economics or have a certification related to cost analysis (DAWIA or CCEA)

Why work at APL?

The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation’s most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates.

At APL, we celebrate our differences and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL’s campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at www.jhuapl.edu/careers.

 

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