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Probabilistic Programming Bayesian Jobs in Colorado

Slope

Software Engineer, Rust

Broomfield, CO · On-site

$165 - $218/hr

Design and implement robust filters, estimators, and probabilistic reasoning systems that enable ... programming patterns in Rust. * Experience in state estimation, including Bayesian filtering ...

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Probabilistic Programming Bayesian information

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 are the key skills and qualifications needed to thrive as a probabilistic programming Bayesian specialist?

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 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 Colorado?

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What job categories do people searching Probabilistic Programming Bayesian jobs in Colorado look for?

The top searched job categories for Probabilistic Programming Bayesian jobs in Colorado are:

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Cities in Colorado with the most Probabilistic Programming Bayesian job openings:

Probabilistic Programming Bayesian jobs near you

Lead Data Scientist, Appraisal & Pricing

Amplio

Denver, CO

Full-time

Medical, Dental, Vision, PTO

Re-posted 5 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

Apply