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Probabilistic Modeling Jobs in Michigan (NOW HIRING)

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 · On-site

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

Detroit, MI · Remote

$18 - $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

Ann Arbor, MI · Remote

$18 - $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

$18 - $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 ...

Ford Motor Company

Perception Systems Engineer

Dearborn, MI · On-site

$99K - $192K/yr

Knowledge of probabilistic filtering (e.g., Kalman Filters, Particle Filters) and spatial mapping ... modeling and/or requirements management tools (SysML/MagicDraw, Polarion, JAMA, DOORS, etc.) and ...

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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.

Which 3 jobs will survive AI?

Probabilistic modeling is a specialized field within data science and machine learning. Jobs that require advanced analytical skills, such as data scientists, machine learning engineers, and quantitative analysts, are likely to persist as they involve complex problem-solving and domain expertise that AI tools complement rather than replace. Continuous learning and proficiency with statistical tools and programming languages like Python or R are essential for these roles.

What is probabilistic modelling?

Probabilistic modeling is a technique used in probabilistic modeling roles to represent uncertainty and variability in data through mathematical models that incorporate probability distributions. It involves designing models that can predict outcomes and infer hidden variables, often using tools like Bayesian inference and statistical analysis. These skills are essential for data scientists and statisticians working with complex, uncertain data environments.

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 professions make 500,000 a year?

In probabilistic modeling, senior roles such as quantitative researchers, data science directors, and machine learning engineers at large tech firms or financial institutions can earn $500,000 or more annually. These positions typically require advanced degrees, extensive experience, and expertise in statistical methods, programming, and data analysis tools. Compensation often includes base salary, bonuses, and stock options, especially in high-growth or competitive industries.

What professions make 200,000 a year without a degree?

Professions related to probabilistic modeling, such as data scientists, machine learning engineers, and quantitative analysts, can reach or exceed $200,000 annually often through experience, specialized skills, and industry demand. These roles typically require strong programming, statistical, and analytical skills, and some may be self-taught or gained through certifications rather than formal degrees.

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
Industry Solutions Product Manager

Industry Solutions Product Manager

ToolsGroup

Detroit, MI • On-site

Full-time

Re-posted 3 days ago

Job description

About ToolsGroup 

ToolsGroup delivers AI-powered supply chain planning solutions that help companies forecast demand, optimize inventory, and plan supply in complex, volatile environments. We partner with global customers to increase service levels, reduce working capital, and respond faster to disruption - by combining deep domain expertise with probabilistic modeling, advanced optimization, and modern SaaS delivery. 
 

The Opportunity  

ToolsGroup serves hundreds of manufacturers globally for their Demand Forecasting, Inventory Optimization and Replenishment needs. As Product Manager for our manufacturing solutions, you will drive the strategy around serving this critical market and the delivery of high-impact capabilities that address core customer challenges, including  planning around BOM complexity, multi-site production networks, and service expectations that make inventory decisions economically outsized (line-down risk, missed delivery penalties, premium freight, expediting). 
 

The Role 

As an Industry Solutions Product Manager (Industrial / Discrete Manufacturing), you will define and drive ToolsGroup’s market-facing product priorities for discrete manufacturing, including industrial equipment, components, automotive/assembly, high-mix manufacturing, aftermarket service parts, and related discrete environments. 
 

You will: 

  • Own the “manufacturing POV” for product discovery and prioritization (what matters, why it matters, and what to build next). Map “day-in-the-life” workflows for planners, supply chain leaders, and operations stakeholders in discrete manufacturing.  

  • Translate manufacturing workflows into product bets with clear problems, measurable outcomes, and actionable roadmap candidates. Capture market needs, run structured feedback loops with customers and partners and translate insights into prioritized product requirements that drive development work.   

  • Partner with go-to-market teams (Sales, marketing, etc.) to shape value propositions and brand promise. Engage as a key subject matter expert in strategic customer conversations—discovery, value engineering, solution validation, and roadmap discussions. 

  • Operate hands-on with AI tools to accelerate discovery, prototype workflows, create user journeys, PRDs, acceptance criteria in partnership with design/engineering) and validate solution direction with customers and internal teams. 

  • Create Repeatable “Manufacturing-First” Assets including product templates, demo storylines, and ROI narratives. Advise on complementary services offerings. 

  • Drive cross-functional execution to deliver market compelling value, support enablement and RFP/RFI inputs and customer-facing engagements.  
     

What You Bring (Minimum Qualifications) 

Experience (Required) 

  • 7+ years of experience either: 
    A) Working in or with industrial/discrete manufacturing organizations in roles tied to manufacturing planning/operations (supply chain planning, production planning, materials, inventory, operations excellence, digital transformation), and you have reached a level where you were responsible for defining key processes/solutions used in core manufacturing workflows; OR 
    B) Building enterprise SaaS products used by manufacturers for inventory optimization, production planning, APS, MRP, S&OP / IBP, or adjacent planning/optimization domains.  
     

Product Craft + Communication (Required) 

  • Proven ability to translate messy, real-world workflows into clear product direction: problem statements, requirements, prioritization logic, and success metrics.   

  • Customer-facing confidence: you can lead discovery sessions, challenge assumptions professionally, and communicate value to practitioners and executives.   

  • Strong written and verbal communication—able to move between executive narratives and detailed workflows.   

AI Comfort (Required) 

  • Comfortable using AI tools to accelerate product work (research synthesis, requirements drafting, prototyping, workflow exploration), while maintaining rigor and validation discipline.    
     

Preferred / “Nice to Have” 

  • Experience with (or strong familiarity with) supply chain planning and APS ecosystems—how manufacturers integrate planning with ERP/MES and surrounding systems, S&OP, Inventory management/optimization. 

  • Background in implementing, supporting, or product-managing planning solutions across manufacturing contexts (production planning/scheduling, inventory optimization, S&OP/IBP).  

  • Comfort collaborating with Design on prototypes and with Engineering/Data Science on feasibility tradeoffs and instrumentation. 

Applying to this job the candidate consents that his/her data are treated by ToolsGroup in compliance with the GDPR n. 2016/679 GDPR and Transparency Document

U.S. applicant notice: This employer participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. ToolsGroup is CCPA/CPRA compliant.

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