Synthesize multiple, low-fidelity 3rd-party signals into a single high-fidelity trend report using Bayesian aggregation or other methods * Data transformation: Apply quasi-experimental designs (e.g ...
Synthesize multiple, low-fidelity 3rd-party signals into a single high-fidelity trend report using Bayesian aggregation or other methods * Data transformation: Apply quasi-experimental designs (e.g ...
NIST PREP Postdoctoral Research Physicist
Gaithersburg, MD · On-site
$85K - $95K/yr
The candidate will also integrate Bayesian algorithms for efficient measurements and develop automated measurement systems. The candidate will undertake numerical modelling of spin dynamics to ...
NIST PREP Postdoctoral Research Physicist
Gaithersburg, MD · On-site
$85K - $95K/yr
The candidate will also integrate Bayesian algorithms for efficient measurements and develop automated measurement systems. The candidate will undertake numerical modelling of spin dynamics to ...
Postdoctoral Fellow (PREP0004018)
Gaithersburg, MD · On-site
$53K - $72K/yr
The candidate will also integrate Bayesian algorithms for efficient measurements and develop automated measurement systems. The candidate will undertake numerical modelling of spin dynamics to ...
Postdoctoral Fellow (PREP0004018)
Gaithersburg, MD · On-site
$53K - $72K/yr
The candidate will also integrate Bayesian algorithms for efficient measurements and develop automated measurement systems. The candidate will undertake numerical modelling of spin dynamics to ...
Apply Bayesian approaches and small area estimation techniques to address sub-national and hard-to-reach population inference challenges. * Own the statistical design process end-to-end: crafting ...
Apply Bayesian approaches and small area estimation techniques to address sub-national and hard-to-reach population inference challenges. * Own the statistical design process end-to-end: crafting ...
Apply Bayesian approaches and small area estimation techniques to address sub-national and hard-to-reach population inference challenges. * Own the statistical design process end-to-end: crafting ...
Apply Bayesian approaches and small area estimation techniques to address sub-national and hard-to-reach population inference challenges. * Own the statistical design process end-to-end: crafting ...
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
Quick apply
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
Quick apply
... Bayesian), Markov-Chain modelling, TensorFlow, Linear Algebra, R, SAS, NLP.
Apply statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to maximize probability of detection across varying noise, interference, and target ...
Apply statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to maximize probability of detection across varying noise, interference, and target ...
Apply statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to maximize probability of detection across varying noise, interference, and target ...
Apply statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to maximize probability of detection across varying noise, interference, and target ...
Apply statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to maximize probability of detection across varying noise, interference, and target ...
Apply statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to maximize probability of detection across varying noise, interference, and target ...
Apply statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to maximize probability of detection across varying noise, interference, and target ...
Apply statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to maximize probability of detection across varying noise, interference, and target ...
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 ...
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 ...
Estimation techniques including Bayesian and non-Bayesian estimators, performance measures such as bias and mean-square error and bounds on estimator performance such as the Cramer-Rao lower bound.
Estimation techniques including Bayesian and non-Bayesian estimators, performance measures such as bias and mean-square error and bounds on estimator performance such as the Cramer-Rao lower bound.
Estimation techniques including Bayesian and non-Bayesian estimators, performance measures such as bias and mean-square error and bounds on estimator performance such as the Cramer-Rao lower bound.
Estimation techniques including Bayesian and non-Bayesian estimators, performance measures such as bias and mean-square error and bounds on estimator performance such as the Cramer-Rao lower bound.
Bayesian information
See Washington salary details
$160.5K - $162.7K
7% of jobs
$162.7K - $164.8K
10% of jobs
$166.7K is the 25th percentile. Wages below this are outliers.
$164.8K - $167K
10% of jobs
$167K - $169.1K
10% of jobs
$169.1K - $171.2K
10% of jobs
The median wage is $172.1K / yr.
$171.2K - $173.4K
10% of jobs
$173.4K - $175.5K
10% of jobs
$177.5K is the 75th percentile. Wages above this are outliers.
$175.5K - $177.7K
10% of jobs
$177.7K - $179.8K
10% of jobs
$179.8K - $182K
10% of jobs
$182K - $184.1K
4% of jobs
$160.5K
$171.8K
$184.1K
How much do bayesian jobs pay per year?
What are the typical projects or challenges faced in a Bayesian-focused role?
In a Bayesian role, you’ll often work on projects involving probabilistic modeling, uncertainty quantification, and predictive analytics for real-world decision-making. Common challenges include structuring prior distributions, ensuring computational efficiency for complex models, and clearly explaining Bayesian results to non-technical stakeholders. You might collaborate closely with data engineers, domain experts, and business analysts to refine models and translate findings into actionable recommendations. This role offers the opportunity to tackle diverse analytical problems across industries like healthcare, finance, or tech, supporting ongoing professional growth and learning.
What is a Bayesian job?
A Bayesian job typically involves applying Bayesian statistics, probabilistic modeling, and inference techniques to analyze data and make decisions under uncertainty. Professionals in this field use Bayes' theorem to update beliefs based on new evidence, often working in areas like machine learning, finance, healthcare, and research. Common roles include Bayesian statisticians, data scientists, and researchers who build probabilistic models to improve predictions and decision-making.
What are the key skills and qualifications needed to thrive in the Bayesian position, and why are they important?
To thrive as a Bayesian (typically a Bayesian Data Scientist or Statistician), you need a strong background in probability theory, statistical modeling, and mathematics, often with an advanced degree in statistics, data science, or a related quantitative field. Experience with programming languages such as Python or R, Bayesian analysis libraries (e.g., Stan, PyMC), and familiarity with statistical software are commonly required. Analytical thinking, collaborative teamwork, and the ability to communicate complex results clearly are valuable soft skills in this role. These abilities are essential for designing robust models, interpreting data accurately, and delivering actionable insights to interdisciplinary teams.
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Meta rating
7.5
Based on 44 frontline employees who took The Breakroom Quiz
124th of 192 rated software companies
Job description
Competitive Intelligence Research Responsibilities:
- Market Strategy: Influence organization-level product direction through data-driven narratives and an in depth understanding of the market landscape. Demonstrated experience operating at scale, and across ambiguous, environments with working knowledge of econometrics. As a quantitative market-strategist, you will blend practical and applied understanding with technical expertise, including pressure-testing data for quality, reliability, understanding data-biases and being solution driven
- Analytics Leadership: Conduct advanced analyses with 3P datasets, develop statistical models and forecasts, and deliver actionable insights that informs market and business strategy. These include, but are not limited to:
- Data onboarding: Identify, onboard, and rigorously evaluate 3P datasets to determine their signal-to-noise ratio and predictive power
- Data triangulation: Triangulate data from many sources of imperfect information. Synthesize multiple, low-fidelity 3rd-party signals into a single high-fidelity trend report using Bayesian aggregation or other methods
- Data transformation: Apply quasi-experimental designs (e.g., synthetic control, diff-in-diff) to isolate the impact of exogenous market shocks and competitor actions on internal performance metrics, using 3rd-party behavioral and economic datasets
- Insight and implications: Apply guidance from such analyses to increase the accuracy of forecasts and better understand market trends
- Technical & Methodological Expertise: Act as a recognized professional in a technical or methodological area (e.g., causal inference, bayesian aggregation), driving the adoption of advanced methods and organization-wide best practices that raise the bar for the entire team
- Data Governance & Quality: Ensure data privacy, security, and compliance with organizational standards. Champion data quality frameworks and documentation practices that enable credible reproducible analyses
- Resourceful, adaptable professional with a bias for action
Minimum Qualifications:
- Bachelors degree and a minimum of 6 years of work experience (minimum of 4 years with a Ph.D.) in business intelligence, product analytics, or economic or strategy consulting in a technology environment with increasing scope and impact
- Demonstrated skill to ethically source, validate, and synthesize high-signal insights from people (e.g., stakeholder interviews, skilled conversations, field research, and relationship-based information gathering) while maintaining high standards for privacy, consent, and integrity
- Proficiency in AI-powered tools: Demonstrate working knowledge of Generative AI technologies (e.g., LLM and AI agents) and experience designing, prompting, and orchestrating AI systems (e.g., prompt engineering) to automate data analyses, synthesize insights, and execute multi-step analytical tasks (e.g., prompting agent to clean datasets, build visualizations)
- Practical working understanding of data-analytics tools, and direct experience managing, analyzing, manipulating and interpreting 1P and external 3P datasets
- Experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), and/or statistical/mathematical software (e.g., R)
- Proven experience with statistical analysis including causal inference (e.g., randomized control trials, quasi-experimentation such as synthetic control, diff-in-diff, meta-analyses), and/or bayesian aggregation (e.g., bayesian pooling, hierarchical modeling)
- Demonstrated communication skills and experience presenting complex findings to both technical and non-technical stakeholders
- Demonstrated experience thriving in ambiguous environments and shape new analytics organizations or products
Preferred Qualifications:
- Master's or Ph.D. Degree in a quantitative field such as Quantitative Economics or Political Science, Operations Research, Data Science, Computer Science, Physics, Business, or Mathematics
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$177,000/year to $247,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
About Meta
Sourced by ZipRecruiter
Industry
Internet and it, media and telecom and software development
Company size
10,000+ Employees
Headquarters location
Menlo Park, CA, US