1

Probabilistic Programming Bayesian Jobs in Washington

Experience with Monte Carlo simulation and probabilistic modeling * Understanding of risk ... Experience with Bayesian networks or causal modeling * Knowledge of portfolio risk aggregation ...

Experience with Monte Carlo simulation and probabilistic modeling * Understanding of risk ... Experience with Bayesian networks or causal modeling * Knowledge of portfolio risk aggregation ...

Experience with Monte Carlo simulation and probabilistic modeling * Understanding of risk ... Experience with Bayesian networks or causal modeling * Knowledge of portfolio risk aggregation ...

Assistant Professor

Washington, DC · On-site

$110K - $125K/yr

Applied Statistics, Probabilistic/Bayesian Machine Learning, Deep Learning, Stochastic Processes ... programming (e.g., Python/R), and data visualization. • Evidence of online/hybrid teaching ...

Probabilistic Programming Bayesian information

What are the key skills and qualifications needed to thrive as a Probabilistic Programming Bayesian specialist, and why are they important?

To thrive as a Probabilistic Programming Bayesian specialist, you need a strong background in statistics, probability theory, and Bayesian inference, often supported by a degree in mathematics, statistics, computer science, or a related field. Expertise with probabilistic programming languages (such as Stan, PyMC, or TensorFlow Probability) and familiarity with statistical modeling software are also essential. Analytical thinking, problem-solving, and effective communication skills help translate complex models into actionable insights and collaborate with interdisciplinary teams. These skills and qualities are crucial for developing robust, interpretable models that inform decision-making in research and industry applications.

What are the typical challenges faced by professionals working in Probabilistic Programming with a Bayesian focus, and how can they be addressed?

Professionals working in Probabilistic Programming with a Bayesian focus often encounter challenges related to model complexity, computational efficiency, and communicating results to non-technical stakeholders. Building accurate Bayesian models requires careful selection of priors and an understanding of underlying data distributions, which can be demanding without robust domain expertise. Additionally, computational demands can be high, especially for large datasets or complex hierarchical models, making efficient sampling and approximation methods essential. Collaborating closely with domain experts and leveraging modern probabilistic programming frameworks can help address these challenges and ensure practical, interpretable results.

What is probabilistic programming in the context of Bayesian statistics?

Probabilistic programming in the context of Bayesian statistics refers to writing computer programs that use probability distributions and Bayesian inference to model uncertainty and learn from data. These programs allow users to define complex probabilistic models using code, making it easier to specify, fit, and analyze Bayesian models. Probabilistic programming languages, such as Stan, PyMC, or Edward, provide tools to automate inference, enabling practitioners to focus on modeling rather than mathematical derivations. This approach is widely used in fields like machine learning, data science, and scientific research to handle uncertainty and make predictions.

What is the difference between Probabilistic Programming Bayesian vs Data Scientist?

AspectProbabilistic Programming BayesianData Scientist
Required credentialsBackground in statistics, probability, programmingStatistics, computer science, or related degree
Work environmentResearch, modeling, algorithm developmentData analysis, visualization, business insights
Industry usageAI, machine learning, research projectsBusiness, finance, tech, healthcare

Probabilistic Programming Bayesian focuses on developing models using Bayesian methods and probabilistic programming languages, often in research or AI development. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles require statistical knowledge, Bayesian programmers specialize in probabilistic modeling, whereas Data Scientists apply a broader set of data analysis techniques.

What are popular job titles related to Probabilistic Programming Bayesian jobs in Washington? For Probabilistic Programming Bayesian jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Probabilistic Programming Bayesian jobs in Washington look for? The top searched job categories for Probabilistic Programming Bayesian jobs in Washington are:
What cities in Washington are hiring for Probabilistic Programming Bayesian jobs? Cities in Washington with the most Probabilistic Programming Bayesian job openings:
2026 PhD Graduate - Statistics and Data Science

2026 PhD Graduate - Statistics and Data Science

Johns Hopkins Applied Physics Laboratory

Laurel, MD • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 14 days ago


Johns Hopkins Applied Physics Laboratory rating

9.9

Company rating: 9.9 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

1st of 56 rated research


Job description

Description
Do you enjoy exploring and analyzing data to find data-driven solution to complex problems?
Do you want to contribute to work that is crucial to maintaining our national security and strength?
Are you continuously searching for new ways to grow your knowledge and improve your skills?
If you are graduating with a PhD in Statistics, Physics, Mathematics, Computer Science, or a related field, we would love to have you join our team! We are seeking a new PhD graduate with expertise in statistics to support multi-disciplinary teams performing a variety of quantitative tasks for defense and national security applications. You will be joining a varied team of engineers, software developers, statisticians, data scientists, and analysts who are committed to advancing the state-of-the-art in performance evaluation of the nation's strategic weapons systems throughout their lifecycle. We believe in continually growing our capabilities and cultivating a work environment that embraces innovation, integrity, trust, and teamwork.
As a member of our team, you will...
  • Work with multi-disciplinary teams to support development of data collection, processing, and analysis efforts to assess the performance of a number of systems supporting the Navy and Air Force.
  • Develop and evaluate statistical models for complex defense applications, including uncertainty quantification, inference, forecasting, and decision support.
  • Apply statistical reasoning for data-driven studies, selecting appropriate methods (e.g., Bayesian or frequentist approaches) based on the problem.
  • Quantify and communicate uncertainty, assumptions, and limitations to support sound decision-making in complex, real-world setting.
  • Use internal funding opportunities to shape the direction of future research.
  • Communicate technical knowledge by articulating ideas clearly through papers and presentations to technical staff, management, and government decision makers.

Qualifications
You meet our minimum qualifications for the job if you...
  • Have a PhD in Data Science, Statistics, Physics, Mathematics, Computer Science or a related field.
  • Demonstrate strong interpersonal skills and the ability to work independently and on a team.
  • Have strong foundations in statistical inference, probability, statistical modeling, and experimental design.
  • Have experience using scientific programming tools such as Python, R, or similar languages for quantitative analysis.
  • Have experience investigating and adapting modern statistical and computational methods to address emerging analysis challenges in sparse, noisy, or high-dimensional data setting.
  • Demonstrate experience with statistical modeling, inference, or experimental design approaches such as Bayesian methods, regression, causal inference, or hypothesis testing...
  • Are able to obtain Interim Secret level security clearance by your start date and can ultimately obtain Top 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 will go above and beyond our minimum requirements if you...
  • Have experience in project management or leading technical teams.
  • Have experience in writing technical proposals, particularly to government research projects.
  • Have experience mentoring students, teaching, or communicating complex technical concepts in academic, research, or professional settings.
  • Have contributed to peer-reviewed publications, technical reports, or presentations in statistics, machine learning, applied mathematics, or related fields.
  • Have experience using probabilistic programming frameworks such as Stan, PyMC, or similar tools.
  • Have research or professional experience developing reproducible analytical workflows, computational research tools, or statistical methodologies for complex quantitative problems.

About Us
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 of perspectives 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 http://www.jhuapl.edu/careers.
All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law. APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact Accommodations@jhuapl.edu.
The referenced pay range is based on JHU APL's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and/or contractual designation. Additional compensation may be provided in the form of a sign-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development. Applications are accepted on a rolling basis.
Minimum Rate
$105,000 Annually
Maximum Rate
$245,000 Annually