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Probabilistic Programming Bayesian Jobs in Pennsylvania

... probabilistic approaches. * Architect and integrate complex combinations of motion planning and ... Experience with Bayesian modeling and inference techniques for decision making under uncertainty.

Senior Motion Planning Engineer

Pittsburgh, PA · On-site +1

$168K - $225K/yr

Experience with probabilistic models, including but not limited to Gaussian mixture models, Hidden ... Experience with Bayesian modeling and inference techniques for decision making under uncertainty.

Experience with probabilistic models, including but not limited to Gaussian mixture models, Hidden ... Experience with Bayesian modeling and inference techniques for decision making under uncertainty.

Experience with probabilistic models, including but not limited to Gaussian mixture models, Hidden ... Experience with machine learning techniques (such as Bayesian modeling and inference techniques ...

Experience with probabilistic models, including but not limited to Gaussian mixture models, Hidden ... Experience with machine learning techniques (such as Bayesian modeling and inference techniques ...

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 Pennsylvania? For Probabilistic Programming Bayesian jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Probabilistic Programming Bayesian jobs in Pennsylvania look for? The top searched job categories for Probabilistic Programming Bayesian jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Probabilistic Programming Bayesian jobs? Cities in Pennsylvania with the most Probabilistic Programming Bayesian job openings:
Infographic showing various Probabilistic Programming Bayesian job openings in Pennsylvania as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Data Scientist - Research and Development

Pittsburgh Associates

Pittsburgh, PA • On-site

Full-time

Posted 9 days ago


Job description

The Pirates Why

The Pittsburgh Pirates are a storied franchise in Major League Baseball who are reinventing themselves on every level. Boldly and relentlessly pursuing excellence by:

  • purposefully developing a player and people-centered culture;
  • deeply connecting with our fans, partners, and colleagues;
  • passionately creating lifetime memories for generations of families and friends; and
  • meaningfully impacting our communities and the game of baseball.

At the Pirates, we believe in the power of a diverse workforce and strive to create an inclusive culture centered in Passion, Innovation, Respect, Accountability, Teamwork, Empathy, and Service.

Job Summary

As a Data Scientist on the Pirates Research & Development team, you will help transform a wealth of baseball data — from box scores and player tracking to video and biomechanics — into actionable insights that drive the Pirates to make better, faster acquisition, development, and deployment decisions. You will work closely with other data scientists, analysts, and software engineers across Baseball R&D as well as other stakeholders across Baseball Operations (scouts, coaches, player development, front office) to turn your statistical and machine learning models into actionable decision tools.

Responsibilities:

  1. Design, build, validate, and deploy statistical and/or machine-learning models to support all facets of baseball operations, including scouting, player acquisition, player development, and on-field decision making.
  2. Build tools, prototypes, and visualizations to translate complex data and model results into insights understandable by coaches, players, and decision-makers.
  3. Communicate results and insights clearly to both technical and non-technical audiences.
  4. Partner with data engineers to build scalable data pipelines and maintain data quality.
  5. Stay abreast of new data sources, analytical techniques, and research.
  6. Help the organization experiment, learn, and iterate.

Qualifications

We recognize that no candidate will meet every qualification listed below. If you are excited about this role and believe you can add value to our work, we encourage you to apply even if your experience does not align perfectly with every requirement.

Required:

  1. Degree (or equivalent experience) in a quantitative discipline (e.g., Statistics, Computer Science, Mathematics, Economics, Machine Learning, Biomechanics, Engineering, Operations Research).
  2. Demonstrated experience applying complex statistical and/or machine learning tools to real-world problems.
  3. Demonstrated proficiency in a programming language such as Python or R for data analysis and modeling.
  4. Demonstrated ability to communicate complex quantitative concepts clearly, both written and verbally.
  5. Demonstrated experience collaborating with others on data science projects.
  6. Authorized to work lawfully in the United States.

Desired:

  1. Familiarity with advanced statistical techniques (e.g., fixed-effect / random-effect models, generalized additive models, Bayesian modeling, probabilistic programming).
  2. Experience with machine-learning / deep-learning frameworks (e.g., PyTorch, Tensorflow), especially applied to high-dimensional, spatiotemporal, or biomechanical data.
  3. Background in computer vision, biomechanics, sports-science, or modeling of dynamic physical systems.
  4. Prior experience in sports analytics context; baseball is a plus.
  5. Experience with database languages (e.g., SQL) and working with large / relational datasets.

Equal Opportunity Employer

The Pittsburgh Pirates are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status or any other characteristic protected by law.