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Probabilistic Modeling Jobs (NOW HIRING)

Snowflake

Senior Data Scientist

Menlo Park, CA · On-site

$156K - $224K/yr

Design and implement advanced time-series and probabilistic models (e.g., hierarchical models, state-space models, Bayesian approaches, multivariate forecasting). * Contribute to internal tooling and ...

Yum Brands

Sr Machine Learning Engineer

Irvine, CA

$112K - $154K/yr

Experience with Bayesian or probabilistic modeling frameworks such as PyMC or ArviZ. * Familiarity with MLflow, Hydra/OmegaConf, FastAPI, or similar ML platform tooling. * Experience supporting deep ...

Fisec Global

Gen AI Engineer

Atlanta, GA · On-site

Strong background in machine learning, deep learning, and probabilistic modeling. * Proficiency in modern data science tools and frameworks, such as PyTorch, Tensorflow, JAX, Scikit-learn, and Keras.

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Probabilistic Modeling information

What is the difference between Probabilistic Modeling vs Data Scientist?

AspectProbabilistic ModelingData Scientist
Required CredentialsDegree in statistics, mathematics, or related fields; knowledge of probability theoryDegree in computer science, statistics, or related fields; programming skills
Work EnvironmentResearch-focused, often in analytics or data science teamsCross-functional teams, including business, engineering, and analytics
Industry UsageUsed in analytics, finance, healthcare, and research for modeling uncertaintyApplied across industries for data analysis, predictive modeling, and decision-making

Probabilistic Modeling focuses on developing models based on probability theory to understand uncertainty, while Data Scientists utilize a broader set of skills including programming, data analysis, and machine learning to extract insights from data. Both roles often overlap but serve different primary purposes within data-driven organizations.

What is probabilistic modeling?

Probabilistic modeling is a mathematical framework used to represent uncertain events or data by using probability distributions. Instead of giving a single outcome, it accounts for variability and randomness, allowing predictions and inferences even when information is incomplete or ambiguous. Probabilistic models are widely used in fields like statistics, machine learning, finance, and engineering to analyze data, make forecasts, and support decision-making under uncertainty.

What is probabilistic modelling?

Probabilistic modeling is a technique used in probabilistic modeling roles to develop mathematical models that incorporate uncertainty and randomness. It involves using statistical methods and tools like Bayesian inference or Markov processes to analyze data and make predictions. Professionals in this field often work with programming languages such as Python or R and require strong analytical skills.

What jobs make $1,000,000 a year?

In the field of probabilistic modeling, highly experienced data scientists, machine learning engineers, or quantitative researchers working in finance, hedge funds, or tech companies can earn $1,000,000 or more annually. These roles often require advanced degrees, specialized skills in statistical analysis and programming, and experience with large-scale data and modeling tools. Compensation at this level typically includes base salary, bonuses, and equity components.

What are the key skills and qualifications needed to thrive as a Probabilistic Modeler, and why are they important?

To thrive as a Probabilistic Modeler, you need a strong background in mathematics, statistics, and probability theory, often supported by a degree in applied mathematics, statistics, or a related field. Proficiency with programming languages like Python or R, and experience with statistical modeling tools and software such as TensorFlow or PyMC, are typically required. Strong analytical thinking, problem-solving abilities, and effective communication skills help translate complex models into actionable insights. These skills are vital for designing accurate models, interpreting uncertainty, and supporting data-driven decisions across various industries.

What jobs pay 500,000 a year in the US?

In the field of probabilistic modeling, senior roles such as Lead Data Scientist or Quantitative Research Director can reach or exceed $500,000 annually, especially in finance, technology, or consulting firms. These positions typically require advanced skills in statistical analysis, machine learning, and programming, along with extensive experience and often a master's or Ph.D. degree.

What jobs make $10,000 a month without a degree?

In probabilistic modeling and related data science roles, professionals can earn $10,000 or more monthly through freelance consulting, specialized contract work, or high-demand positions in finance, tech, or analytics that value skills over formal degrees. Success often depends on expertise in statistical software, programming languages like Python or R, and a strong portfolio of projects. Building a reputation and gaining experience can lead to high earnings without a traditional degree.

What are some common challenges faced by professionals in probabilistic modeling roles, and how can they be managed?

Professionals in probabilistic modeling often encounter challenges such as working with incomplete or noisy data, choosing the right model complexity, and ensuring model interpretability for stakeholders. Managing these challenges involves strong statistical knowledge, regular collaboration with domain experts, and effective communication to translate complex results for non-technical team members. Staying up-to-date with the latest tools and methodologies, and participating in peer reviews, can also help maintain model accuracy and reliability.
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Probabilistic Modeling jobs near you
Infographic showing various Probabilistic Modeling job openings in the United States as of June 2026, with employment types broken down into 4% Internship, 92% Full Time, and 4% Contract. Highlights an 82% In-person, and 18% Remote job distribution.
Applied Research Scientist, Proactive Intelligence, - Agentic Systems and Generative Modeling

Applied Research Scientist, Proactive Intelligence, - Agentic Systems and Generative Modeling

Apple

Cupertino, CA • On-site

Full-time

Posted 11 days ago

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

AI represents a big opportunity to elevate Apple's products and experiences for billions of people globally. We are looking for Applied Research Scientists with a background and interest in Agentic Systems. You will be leveraging state-of-the-art Generative models to ship extraordinary products, services, and customer experiences for the iPhone, Mac, Apple Watch, iPad and more. The mission of Proactive Intelligence is to improve Apple platforms by better understanding, anticipating and adapting to user behavior by using machine learning to build phenomenal features that are built right into Apple platforms. Our team provides an opportunity to be part of an incredible research and engineering organization within Apple. The ideal candidate for this role will have industry experience working on a range of modeling problems, including Sequential Decision Making, Reinforcement Learning, Autonomous Systems, Learning from Human Preferences and Training Large Language Models (LLMs). Working knowledge of large-scale data processing especially with structured data, probabilistic modeling and statistics will broaden your role and effectiveness in this position.
As an Applied Research Scientist on our team, you will design and implement ML algorithms that process data in different Apple products. You will train Generative AI models and Agentic systems using deep reinforcement learning to solve hard problems. Where necessary, you will also work on integrating ML/RL frameworks into our products to train large-scale agents and leverage cloud services to enable scalable and distributed training/simulation of agent behaviors. You will communicate advanced ideas to a focused team of researchers in the spirit of developing innovative tools and metrics that change the way we look at problems. You will work closely with other cross-functional teams to align messaging, contribute to roadmaps and contribute software back into different repos for proper integration with core systems. You will write clean, maintainable and production code with appropriate documentation and tests. You will contribute to architecture decisions, design reviews and peer code reviews!
Strong programming skills in Python and/or C++ with 3+ years of experience in using these languages for machine learning (ML) modeling and applied researchM.S. or PhD in Computer Science, or a related fields such as Electrical Engineering, Robotics, Statistics, Applied Mathematics or equivalent experience. A minimum of 3 years of experience in applied ML and/or product development.Fundamental knowledge of ML concepts and hands-on experience in building deep-learning systemsStrong software engineering skills to create scalable and robust infrastructure for machine-learning data, modeling and evaluation systemsProven ability to train and debug machine-learning systems: defining metrics and datasets, performing error analysis and training models in a modern ML framework
Familiarity with researching current ML literature and math including optimization methods and modeling techniquesPassionate about building extraordinary autonomous systems with Generative AICreative, collaborative and project focused with an ability to work hands-on in multi-functional teamsProficiency in using ML toolkits such as PyTorch, TensorFlow, SkLearn etc.

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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