Strong modeling and experimentation skills (Python, SQL, Bayesian / probabilistic modeling). * Comfort operating with limited data - capable of building and validating proxy-based models. * Blend of ...
Strong modeling and experimentation skills (Python, SQL, Bayesian / probabilistic modeling). * Comfort operating with limited data - capable of building and validating proxy-based models. * Blend of ...
Probabilistic Programming Bayesian information
See Atlanta, GA salary details
$147.6K - $164.3K
5% of jobs
$164.3K - $180.9K
7% of jobs
$180.9K - $197.6K
6% of jobs
$197.6K - $214.2K
1% of jobs
$214.2K - $230.9K
1% of jobs
$230.9K - $247.5K
2% of jobs
$252.5K is the 25th percentile. Wages below this are outliers.
$247.5K - $264.2K
5% of jobs
$264.2K - $280.8K
18% of jobs
The median wage is $283.8K / yr.
$280.8K - $297.5K
18% of jobs
$306.8K is the 75th percentile. Wages above this are outliers.
$297.5K - $314.2K
18% of jobs
$314.2K - $330.8K
17% of jobs
$147.6K
$269.4K
$330.8K
How much do probabilistic programming bayesian jobs pay per year?
As of Jun 27, 2026, the average yearly pay for probabilistic programming bayesian in Atlanta, GA is $269,406.00, according to ZipRecruiter salary data. Most workers in this role earn between $250,500.00 and $310,100.00 per year, depending on experience, location, and employer.
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?
| Aspect | Probabilistic Programming Bayesian | Data Scientist |
|---|---|---|
| Required credentials | Background in statistics, probability, programming | Statistics, computer science, or related degree |
| Work environment | Research, modeling, algorithm development | Data analysis, visualization, business insights |
| Industry usage | AI, machine learning, research projects | Business, 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 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 popular job titles related to Probabilistic Programming Bayesian jobs in Atlanta, GA? For Probabilistic Programming Bayesian jobs in Atlanta, GA, the most frequently searched job titles are:
- Actuarial Science Summer Internship
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What job categories do people searching Probabilistic Programming Bayesian jobs in Atlanta, GA look for? The top searched job categories for Probabilistic Programming Bayesian jobs in Atlanta, GA are:
Other
Medical, Dental, Vision, PTO
Posted 28 days ago
Job description
About the Role
Amplio is building the intelligence layer that powers how manufacturers recover value from surplus equipment. You'll lead the development of our appraisal and pricing capabilities - combining data science with agentic AI to automate and improve valuation decisions at scale.
Key Responsibilities
- Design and iterate models that estimate fair market value, recovery potential, and optimal disposition strategy.
- Leverage agentic AI to automate cataloging and appraisal workflows.
- Support Sales to underwrite buyout offers and predict consignment recovery and capacity utilization.
- Build feedback loops for continuous price optimization and model refinement.
- Collaborate with Product and Ops to turn insights into high-impact pricing and sourcing actions.
Requirements
What You Bring
- 4-8+ years in data science, analytics, or preferably pricing within marketplaces, logistics, or asset-heavy environments.
- Strong modeling and experimentation skills (Python, SQL, Bayesian / probabilistic modeling).
- Comfort operating with limited data - capable of building and validating proxy-based models.
- Blend of technical depth and business intuition; fluent in assumptions, risk, and real-world tradeoffs.
- Curiosity for manufacturing, recommerce, and the circular economy.
Benefits
- Early-hire equity
- Best Medical / Dental / Vision plans
- Parental benefits
- Flexible PTO
- Various stipends
- Clear work-life boundaries