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Probabilistic Programming Bayesian Jobs in Dublin, CA

Avathon

Senior AI Scientist - Planning

Pleasanton, CA

$160K - $240K/yr

Familiarity with probabilistic programming or Bayesian methods for demand sensing * Experience with cloud ML infrastructure (AWS SageMaker, GCP Vertex, or equivalent) * Domain experience in energy ...

Avathon

Senior AI Scientist - Planning

Pleasanton, CA · On-site

Familiarity with probabilistic programming or Bayesian methods for demand sensing * Experience with cloud ML infrastructure (AWS SageMaker, GCP Vertex, or equivalent) * Domain experience in energy ...

Kodiak

Autonomy Systems Software Engineer

San Francisco, CA · On-site

$203K - $241K/yr

Strong programming skills in Python, MATLAB, C++, or C * Hands-on work across multiple of the ... Bayesian inference (e.g., beta and gamma distributions) * Markov models and probabilistic system ...

Walmart

Staff, Data Scientist

Sunnyvale, CA

Engineer for production from day one on Databricks (on GCP) -PySpark+ Delta for distributed ... Bayesian methods, reconciliation across hierarchies, calibrated probabilistic projections, and ...

Snowflake

Senior Data Scientist

Menlo Park, CA · On-site

$156K - $224K/yr

... probabilistic models (e.g., hierarchical models, state-space models, Bayesian approaches ... Engineering, Computer Science) or equivalent practical experience. * 8+ years of experience ...

Walmart

(USA)Staff, Data Scientist

Fremont, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

Hayward, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

Sunnyvale, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

San Mateo, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

Sunnyvale, CA

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

San Jose, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

Milpitas, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

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All JobsProbabilistic Programming Bayesian JobsProbabilistic Programming Bayesian Jobs in Dublin, CA

Probabilistic Programming Bayesian information

See Dublin, CA salary details

$172.9K

$315.5K

$387.4K

How much do probabilistic programming bayesian jobs pay per year?

As of Jun 27, 2026, the average yearly pay for probabilistic programming bayesian in Dublin, CA is $315,502.00, according to ZipRecruiter salary data. Most workers in this role earn between $293,400.00 and $363,200.00 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?

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 job categories do people searching Probabilistic Programming Bayesian jobs in Dublin, CA look for? The top searched job categories for Probabilistic Programming Bayesian jobs in Dublin, CA are:
What cities near Dublin, CA are hiring for Probabilistic Programming Bayesian jobs? Cities near Dublin, CA with the most Probabilistic Programming Bayesian job openings:
Probabilistic Programming Bayesian jobs near you
Infographic showing various Probabilistic Programming Bayesian job openings in Dublin, CA as of June 2026, with employment types broken down into 2% As Needed, 3% Full Time, 87% Part Time, 4% Temporary, and 4% Nights. Highlights an 67% Physical, 2% Hybrid, and 31% Remote job distribution, with an average salary of $315,502 per year, or $151.7 per hour.

Senior AI Scientist - Planning

Avathon

Pleasanton, CA

$160K - $240K/yr

Other

Medical, Retirement

Posted 26 days ago

Job description

About the Role

As a Senior AI Scientist - Planning, you will design and develop the AI and optimization models that power Avathon's planning intelligence - demand forecasting, supply planning, inventory optimization, and S&OP decision support for customers across energy, mining, manufacturing, and logistics.

This role sits at the intersection of AI, ML and operations research. You will build models that work with complex supply chain data - demand history, ERP/MRP transactions, and commercial forecasts across multi-echelon networks with long lead times and constrained capacity. The work is equal parts applied research and production delivery. You will develop novel approaches where standard methods fall short and ship them into a platform used by enterprise customers. You will report into the Data Science org and work closely with product and engineering.

You Will

  • Build demand forecasting models across industrial verticals, applying probabilistic, hierarchical, and intermittent demand methods where history is short or volatile
  • Design supply planning and inventory optimization models across multi-echelon networks with capacity constraints and variable lead times
  • Formulate and solve optimization problems (MIP, LP, constraint programming) for production scheduling, allocation, and resource planning
  • Build simulation and scenario analysis frameworks to support S&OP and integrated business planning workflows
  • Define evaluation metrics, build backtesting pipelines, and run controlled experiments to measure and improve model performance
  • Integrate cross-domain signals from Avathon's Computational Knowledge Graph -- asset health, logistics, procurement - into planning models
  • Own the path from research to production in collaboration with ML Engineering, delivering scalable and monitored services
  • Mentor junior data scientists on technical depth and OR/ML fundamentals

You'll Have

  • Ph.D. or master's degree in operations research, Industrial Engineering, Statistics, Computer Science, Applied Mathematics, or a related quantitative field
  • 10-15 years of industry experience in applied ML, optimization, or Supply Chain planning systems
  • Deep experience in at least two of: demand forecasting, supply/inventory optimization, production scheduling, or S&OP analytics
  • Proficiency in Python with hands-on experience in optimization solvers (Gurobi, CPLEX, OR-Tools, or similar) or forecasting libraries (Prophet, statsmodels, GluonTS, or similar)
  • Strong foundation in statistical modeling with sound judgment on when simple methods outperform complex ones
  • Experience with supply chain and operational data - demand history, ERP/MRP transactions, and planning system outputs
  • Ability to translate business problems into mathematical optimization or ML formulations
  • Experience deploying models into production environments

Preferred Qualifications

  • Ph.D. in Operations Research, Industrial Engineering, Statistics, or a related field
  • Background in supply chain planning platforms or industrial AI (o9, Blue Yonder, Kinaxis, or similar), preferably within the supply chain industry
  • Experience in supply chain planning platforms or industrial AI (o9 Solutions, Blue Yonder, Kinaxis, or similar) preferably from Supply Chain industry.
  • Experience with multi-echelon inventory optimization or network design
  • Familiarity with probabilistic programming or Bayesian methods for demand sensing
  • Experience with cloud ML infrastructure (AWS SageMaker, GCP Vertex, or equivalent)
  • Domain experience in energy, mining, manufacturing, aerospace, or logistics or Track record of publishing or presenting applied OR/ML work as plus

Interview Process

As part of the interview process, you will be asked to complete a technical assessment.

Benefits & Perks

What are the benefits and perks at Avathon? Below are some highlights we offer to our U.S. full-time employees -- we'd love to connect and share more!

  • Evolving culture with the opportunity to drive new ideas and technology
  • Stock Option Grants
  • Medical Coverage and Parental Leave Plans
  • 401k with Employer Match
  • Monthly Technology Allowance
  • Newly renovated office space located near Pleasanton, CA -- including fully stocked beverage and snack areas

Contract and temporary roles are not eligible for the above benefits.

Compensation

Pay Range: $160k - $240k salary annually. Pay for this position is based on a number of factors including geographic location and may vary depending on job-related knowledge, skills, and experience.

Location: This role is not remote. Candidates must be based in the Bay Area, CA and are expected to report to our Pleasanton office 5 days a week.

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