Design and implement Bayesian models and probabilistic frameworks for uncertainty estimation and ... Familiarity with feature engineering, model validation, and performance tuning * Exposure to ML ...
Design and implement Bayesian models and probabilistic frameworks for uncertainty estimation and ... Familiarity with feature engineering, model validation, and performance tuning * Exposure to ML ...
Design and implement Bayesian models and probabilistic frameworks for uncertainty estimation and ... Familiarity with feature engineering, model validation, and performance tuning * Exposure to ML ...
Quick apply
Design and implement Bayesian models and probabilistic frameworks for uncertainty estimation and ... Familiarity with feature engineering, model validation, and performance tuning * Exposure to ML ...
Experimental Design & Bayesian Optimization for New Product Development * Design and apply advanced ... Strong foundation in applied statistics, experimental design, and probabilistic modeling.
Quick apply
Experimental Design & Bayesian Optimization for New Product Development * Design and apply advanced ... Strong foundation in applied statistics, experimental design, and probabilistic modeling.
Experimental Design & Bayesian Optimization for New Product Development * Design and apply advanced ... Strong foundation in applied statistics, experimental design, and probabilistic modeling.
Experimental Design & Bayesian Optimization for New Product Development * Design and apply advanced ... Strong foundation in applied statistics, experimental design, and probabilistic modeling.
Experimental Design & Bayesian Optimization for New Product Development * Design and apply advanced ... Strong foundation in applied statistics, experimental design, and probabilistic modeling.
Experimental Design & Bayesian Optimization for New Product Development * Design and apply advanced ... Strong foundation in applied statistics, experimental design, and probabilistic modeling.
Digital Innovation Engineer
Wilmington, DE · On-site
$132K/yr
Experimental Design & Bayesian Optimization for New Product Development * Design and apply advanced ... Strong foundation in applied statistics, experimental design, and probabilistic modeling.
Digital Innovation Engineer
Wilmington, DE · On-site
$132K/yr
Experimental Design & Bayesian Optimization for New Product Development * Design and apply advanced ... Strong foundation in applied statistics, experimental design, and probabilistic modeling.
Probabilistic Programming Bayesian information
See Berwyn, PA salary details
$147.3K - $163.9K
5% of jobs
$163.9K - $180.5K
7% of jobs
$180.5K - $197.1K
6% of jobs
$197.1K - $213.7K
1% of jobs
$213.7K - $230.3K
1% of jobs
$230.3K - $247K
2% of jobs
$251.9K is the 25th percentile. Wages below this are outliers.
$247K - $263.6K
5% of jobs
$263.6K - $280.2K
18% of jobs
The median wage is $283.1K / yr.
$280.2K - $296.8K
18% of jobs
$306.1K is the 75th percentile. Wages above this are outliers.
$296.8K - $313.4K
18% of jobs
$313.4K - $330K
17% of jobs
$147.3K
$268.8K
$330K
How much do probabilistic programming bayesian jobs pay per year?
As of Jun 8, 2026, the average yearly pay for probabilistic programming bayesian in Berwyn, PA is $268,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $249,900.00 and $309,400.00 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 job categories do people searching Probabilistic Programming Bayesian jobs in Berwyn, PA look for? The top searched job categories for Probabilistic Programming Bayesian jobs in Berwyn, PA are:
What cities near Berwyn, PA are hiring for Probabilistic Programming Bayesian jobs? Cities near Berwyn, PA with the most Probabilistic Programming Bayesian job openings:
Full-time
Posted 6 days ago
Job description
Role: Senior Manager
Location: Philadelphia (Hybrid)
Experience: 5+ years
Full Time Role
Role Overview
We are looking for a Senior Manager - Data Science (Econometrics & Time Series) to lead advanced analytical initiatives for a major Telecommunications client.
This role is heavily focused on econometric modeling, time series analysis, and causal inference, with applications in forecasting, pricing, and customer behavior analytics. The ideal candidate brings deep expertise in statistical modeling and is comfortable working with large-scale data environments.
Key Responsibilities
Must-Have Qualifications
Secondary / Good-to-Have Skills (General Data Science)
Location: Philadelphia (Hybrid)
Experience: 5+ years
Full Time Role
Role Overview
We are looking for a Senior Manager - Data Science (Econometrics & Time Series) to lead advanced analytical initiatives for a major Telecommunications client.
This role is heavily focused on econometric modeling, time series analysis, and causal inference, with applications in forecasting, pricing, and customer behavior analytics. The ideal candidate brings deep expertise in statistical modeling and is comfortable working with large-scale data environments.
Key Responsibilities
- Lead development of time series forecasting models (ARIMA, VAR, state-space models, etc.) for business-critical use cases.
- Apply econometric techniques such as WLS, panel data models, and causal inference methods to solve real-world business problems.
- Design and implement Bayesian models and probabilistic frameworks for uncertainty estimation and decision-making.
- Utilize Markov chains and stochastic processes for modeling sequential or behavioral data.
- Translate business problems into robust analytical frameworks and deliver actionable insights.
- Work with large datasets using Databricks
- Collaborate with stakeholders across business and technical teams to ensure model relevance and impact.
- Mentor junior team members and drive best practices in statistical modeling and experimentation.
Must-Have Qualifications
- Strong foundation in econometrics and time series analysis (this is critical for the role).
- Hands-on experience with:
- Time series models (ARIMA, SARIMA, VAR, forecasting techniques)
- Econometric methods (WLS, regression diagnostics, panel data models)
- Causal inference (A/B testing, quasi-experimental methods)
- Bayesian statistics and probabilistic modeling
- Markov chains or stochastic modeling
- Proficiency in Python along with SQL.
- Experience working with Databricks or similar big data platforms.
- Ability to clearly communicate complex statistical concepts to non-technical stakeholders.
Secondary / Good-to-Have Skills (General Data Science)
- Experience with machine learning models (classification, regression, tree-based models, etc.)
- Familiarity with feature engineering, model validation, and performance tuning
- Exposure to ML pipelines and MLOps concepts
About Lorven technologies
Sourced by ZipRecruiter
Lorven Technologies, headquartered in Plainsboro, New Jersey, United States, is a reputable company in the technology industry, specializing in providing effective IT solutions and consulting services. The company's official website, lorventech.com, offers comprehensive insights into its offerings which include but are not limited to software development, IT consulting, project management, and business analysis. Since its inception, Lorven Technologies has been committed to ensuring efficiency and reliability in delivering IT services to its global clientele, establishing itself as a trusted name in the industry.
Industry
It services
Company size
51 - 200 Employees
Headquarters location
Plainsboro, NJ, US
Year founded
2001