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Probabilistic Modeling Jobs in Michigan (NOW HIRING)

OtterBase

Localization Algorithm Engineer

Detroit, MI · On-site

This role focuses on developing and optimizing localization algorithms using probabilistic methods ... models into efficient, production-ready code, with an emphasis on real-time or near-real-time ...

THOMSON REUTERS

Research Engineer

Ann Arbor, MI · Hybrid

Familiarity with probabilistic models andunderstandthe mathematical concepts underlying machine learning methods * Proficiencyin automation, system monitoring, and cloud-native applications, with ...

THOMSON REUTERS

Lead Research Engineer

Ann Arbor, MI · On-site +1

$100K - $132K/yr

Familiarity with probabilistic models and have an understanding of the mathematical concepts underlying machine learning methods * Demonstrated ability to mentor engineers, elevate team technical ...

Ford Motor Company

Data Scientist

Dearborn, MI · On-site +1

$107K - $182K/yr

... and probabilistic conditions. 1 year of experience with the following skill is required: 1. ... Apply the most relevant quantitative modeling techniques and tools in statistical analysis ...

THOMSON REUTERS

Senior UX Designer

Ann Arbor, MI

Familiarity with model behaviors and limitations-hallucinations, confidence levels, deterministic vs. probabilistic output, and data quality impacts. * Ability to prototype using tools such as Figma ...

Varsity Tutors

Genetics Tutor

Ann Arbor, MI · Remote

$40/hr

Ability to explain linkage analysis, Hardy-Weinberg equilibrium, and gene regulation models while ... Emphasizes probabilistic reasoning and connects genetics to genetic counseling, forensic science ...

Varsity Tutors

Genetics Tutor

Kalamazoo, MI · Remote

$40/hr

Ability to explain linkage analysis, Hardy-Weinberg equilibrium, and gene regulation models while ... Emphasizes probabilistic reasoning and connects genetics to genetic counseling, forensic science ...

Varsity Tutors

Genetics Tutor

Detroit, MI · Remote

$40/hr

Ability to explain linkage analysis, Hardy-Weinberg equilibrium, and gene regulation models while ... Emphasizes probabilistic reasoning and connects genetics to genetic counseling, forensic science ...

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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.
What are popular job titles related to Probabilistic Modeling jobs in Michigan? For Probabilistic Modeling jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Probabilistic Modeling jobs in Michigan look for? The top searched job categories for Probabilistic Modeling jobs in Michigan are:
What cities in Michigan are hiring for Probabilistic Modeling jobs? Cities in Michigan with the most Probabilistic Modeling job openings:
Probabilistic Modeling jobs near you
Infographic showing various Probabilistic Modeling job openings in Michigan as of June 2026, with employment types broken down into 3% Internship, 90% Full Time, and 7% Contract. Highlights an 86% In-person, and 14% Remote job distribution.
Department of Statistics RESEARCH FELLOW

Department of Statistics RESEARCH FELLOW

Michigan Medicine

Ann Arbor, MI • On-site

Full-time

Posted 8 days ago

Michigan Medicine rating

7.4

Company rating: 7.4 out of 10

Based on 72 frontline employees who took The Breakroom Quiz

321st of 1,001 rated hospitals

Job description

Job Summary:
Michigan Medicine is seeking a postdoctoral research fellow in statistical genetics and computational genomics within the Terhorst Lab. The role involves developing scalable methods for complex trait analysis and collaborating with researchers across various fields.
Responsibilities:
• Develop novel statistical and computational methods for ARG-based quantitative genetics
• Analyze large-scale genetic and phenotypic datasets
• Implement scalable software and algorithms for genomic inference
• Collaborate with researchers across statistics, genetics, and computational biology
• Contribute to manuscripts, presentations, and open-source software development
• Participate in interdisciplinary collaborations related to predictive breeding and genome editing
• Travel to the United Kingdom to collaborate with project partners at the University of Edinburgh and the University of Oxford
Qualifications:
Required:
• PhD in statistics, computer science, computational biology, genetics, applied mathematics, or a related quantitative field is required
• Proof of degree completion must be in hand on or before the start date
• Strong programming and computational skills
• Experience with statistical modeling, machine learning, or large-scale data analysis
Preferred:
• Experience with population genetics or statistical genetics
• Familiarity with Bayesian methods, probabilistic modeling, or graphical models
• Experience with scientific computing in Python, JAX, Torch, Julia, C++, or related languages
• Experience with high-performance computing or scalable algorithms
• Interest in interdisciplinary research spanning genomics and evolutionary biology
Company:
Michigan Medicine is a health care system and academic medical center that provides medical education and more. It is a sub-organization of University of Michigan. Founded in 1869, the company is headquartered in Ann Arbor, USA, with a team of 10001+ employees. The company is currently Late Stage.

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