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Probabilistic Modeling Jobs (NOW HIRING)

We are seeking candidates with deep expertise in decision-focused AI, including probabilistic modeling, forecasting, causal inference, simulation-based planning, agentic and multi-agent systems ...

We are seeking candidates with deep expertise in decision-focused AI, including probabilistic modeling, forecasting, causal inference, simulation-based planning, agentic and multi-agent systems ...

... probabilistic modeling, databricks, Ray RLLib, Gymnasium, PettingZoo (MARL). Company : ClifyX provides innovative business solutions which satisfy requirements for mission-critical reliability ...

We are seeking candidates with deep expertise in decision-focused AI, including probabilistic modeling, forecasting, causal inference, simulation-based planning, agentic and multi-agent systems ...

$150 - $210/hr

Simulation engine / Monte Carlo / probabilistic modeling experience * Computer vision or image analysis (for creative intelligence) * Ad-tech or growth engineering background * A/B testing or ...

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Probabilistic Modeling information

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 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 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.

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.

More about Probabilistic Modeling jobs

What cities are hiring for Probabilistic Modeling jobs?

Cities with the most Probabilistic Modeling job openings:

What states have the most Probabilistic Modeling jobs?

States with the most job openings for Probabilistic Modeling jobs include:

Infographic showing various Probabilistic Modeling job openings in the United States as of August 2026, with employment types broken down into 86% Full Time, 11% Part Time, and 3% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution.

Staff AI Research Scientist

Intuit

Mountain View, CA • On-site

$209K - $283K/yr

Full-time

Posted 8 days ago


Intuit rating

8.2

Company rating: 8.2 out of 10

Based on 92 frontline employees who took The Breakroom Quiz

107th of 245 rated software companies


Job description

Intuit is hiring a Staff AI Research Scientist to join the Intuit Foresight team.
In its intrapreneurial function, the Foresight team at Intuit develops and incubates emerging technologies, user experiences, capabilities and products that have the potential to be transformative to businesses and consumers. The mission of the AI Research team is to do cutting edge research in AI topics that directly influence Intuit's portfolio of fintech offerings and position Intuit as a global leader in AI with the goal of powering prosperity around the world. In this role you will advance the state of the art in AI methods that support decision making under uncertainty, including interpretable and trustworthy reasoning systems.
We are seeking candidates with deep expertise in decision-focused AI, including probabilistic modeling, forecasting, causal inference, simulation-based planning, agentic and multi-agent systems, neuro-symbolic AI, fundamentals of deep learning architectures and model training (pre and post) , and reinforcement learning, and LLM based reasoning for real-world business decision workflows.
You will join a high-caliber team of domain experts, data scientists, and machine learning engineers to shape our strategy in this area, and develop advanced capabilities that serve our customers' needs, both current and future.
Responsibilities
• Conducting basic and applied research on probabilistic, causal, and simulation-based models, and on methods that enable LLMs to reason over structured business data
• Engaging with the academic and broader research community through publications, presentations, and collaborations.
• Working towards long-term ambitious research goals that are in alignment with Intuit products, while identifying intermediate milestones
• Working with a cross-functional team of designers, product managers, engineers, researchers, legal experts and more, to translate scientific AI methods into decision-support capabilities that power the customer experiences across various Intuit product offerings.
• Mentoring junior researchers, AI scientists, and interns through goal setting and technical directions that lead to positive outcomes
Qualifications
• PhD in Computer Science/Electrical Engineering/Statistics/Applied Mathematics or a related field with focus on AI/ML/Data Science
• 5+ years of relevant work experience post PhD
• Established track record of publication, ideally demonstrated through first author publications in top tier AI conferences ( e.g. NeurIPS, ICML, ICLR, KDD, ACL, AAAI)
• 8+ years experience in conducting theoretical or applied research with demonstrated capability of defining and driving research projects
• Past experience in disseminating new technical ideas to the larger scientific community through open sourcing, publications, and presentations
• Experience with developing and debugging in Python with Tensorflow, Pytorch, or similar frameworks
• Deep technical expertise in machine learning, with hands-on experience in probabilistic modeling, causal inference, time-series forecasting, reinforcement learning, agentic and multi-agent systems, neuro-symbolic AI, deep learning architectures, or model training.
• Excited about the future of Money, Payments, FinTech, and the transformation these technologies will drive
• Strong interpersonal and communication skills in order to effectively communicate and/or collaborate with other team members as well as product technologists and other stakeholders
• Excellent leadership and communication skills to influence teams and to evangelize research solutions across the company
• Past experience leading a research team and mentoring junior colleagues is a plus
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Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at ). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
The expected base pay range for this position is:
Mountain View $209,500 - $283,500

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