ZipRecruiter

Log In

1

Probabilistic Programming Bayesian Jobs in Illinois

All JobsProbabilistic Programming Bayesian JobsProbabilistic Programming Bayesian Jobs in Illinois

Probabilistic Programming Bayesian information

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 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 Illinois? For Probabilistic Programming Bayesian jobs in Illinois, the most frequently searched job titles are:
What job categories do people searching Probabilistic Programming Bayesian jobs in Illinois look for? The top searched job categories for Probabilistic Programming Bayesian jobs in Illinois are:
What cities in Illinois are hiring for Probabilistic Programming Bayesian jobs? Cities in Illinois with the most Probabilistic Programming Bayesian job openings:
Probabilistic Programming Bayesian jobs near you
Applied Machine Learning Engineer

Applied Machine Learning Engineer

Strata Decision Technology

Chicago, IL • On-site

$117K - $150K/yr

Other

Medical, Life, Retirement, PTO

Posted 4 days ago

Job description

How You'll Make an Impact
As an Applied Machine Learning Engineer, you will collaborate with architects, data scientists, agentic AI developers, platform engineers, and the product team to build advanced AI and ML capabilities into our platform. Your work will drive innovation in generative AI and beyond, integrating and customizing a wide range of machine learning techniques to solve complex problems in healthcare. By developing next-generation AI agents, algorithms, and computation engines, you will help Strata strengthen its market leadership, improve operational efficiency, and support healthcare providers in delivering high-quality care while maintaining financial health.

A Day in the Life

  • Read and translate the latest research (e.g., arXiv papers) into production-ready solutions in Python.

  • Prototype and iterate on machine learning models, focusing on areas such as regression, causal inference, optimization, and vector embeddings.

  • Collaborate with cross-functional teams to embed ML and AI capabilities directly into our software platform.

  • Partner with data scientists to design experiments and apply statistical concepts to real-world data.

  • Optimize, test, and scale ML models to support mission-critical healthcare analytics.

Our Technology Stack
Our core platform is used by more than half of the nation's leading healthcare providers, enabling them to leverage financial, operational, and clinical data. Our AI and ML stack includes:

  • Languages & Libraries: Python, PyTorch, NumPy, Pandas, Polars, PyMC

  • Infrastructure: AWS, Snowflake, Docker, GitHub

  • Techniques & Tools:

    • Regression (with and without Bayesian priors)

    • Vector embeddings, similarity, clustering

    • Core statistics and distributions for EDA

    • Optimization methods (multi-armed bandit, mixed integer programming)

    • Causal inference and probabilistic modeling

What We're Looking For
We're seeking a technically curious engineer who thrives on turning theory into practice. The ideal candidate has:

  • Strong experience implementing ML models in Python.

  • Familiarity with regression, embeddings, causal inference, and optimization techniques.

  • Experience applying statistical methods to exploratory data analysis.

  • Comfort working with modern ML libraries and frameworks.

Bonus points if you have worked with:

  • NLP tasks (LLMs, spaCy, neural networks).

  • Recommender systems, latent factors, matrix factorization.

  • Graph algorithms.

  • Claude Code, Docker, and GitHub.ess computation engine.

Estimated Salary Range: $117,000-150,000
Actual salary will be determined based on factors including, but not limited to, skill set and level of experience. This salary range is a good faith estimate of base pay. Strata also provides discretionary variable pay programs based on role. In addition, Strata provides a comprehensive benefits package including retirement benefits, health and welfare benefits, paid time off, parental leave, life and accident insurance, and other voluntary and well-being benefits.

Apply