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Probabilistic Modeling Jobs in Georgia (NOW HIRING)

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.

Warnerbros

Sr. Principal Data Scientist

Atlanta, GA

Time-series forecasting & probabilistic models * Causal inference, experimentation & uplift modelling * NLP, generative AI & multimodal ML systems * Computer vision & video intelligence pipelines

PrizePicks

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$220K - $280K/yr

Experience implementing reinforcement learning or complex probabilistic models for dynamic pricing, risk management, or fraud detection. * Background in Daily Fantasy Sports (DFS), oddsmaking, or ...

PrizePicks

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$220K - $280K/yr

Experience implementing reinforcement learning or complex probabilistic models for dynamic pricing, risk management, or fraud detection. * Background in Daily Fantasy Sports (DFS), oddsmaking, or ...

loomis

Data Scientist

Suwanee, GA

Apply and evaluate time-series, probabilistic, and machine-learning techniques to improve forecast accuracy and stability. * Own model performance monitoring, drift detection, recalibration ...

loomis

Data Scientist

Suwanee, GA

Apply and evaluate time-series, probabilistic, and machine-learning techniques to improve forecast accuracy and stability. * Own model performance monitoring, drift detection, recalibration ...

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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
Lead Data Scientist, Appraisal & Pricing

Lead Data Scientist, Appraisal & Pricing

Amplio

Atlanta, GA

Other

Medical, Dental, Vision, PTO

Re-posted 15 days ago

Job description

About the Role

Amplio is building the intelligence layer that powers how manufacturers recover value from surplus equipment. You'll lead the development of our appraisal and pricing capabilities - combining data science with agentic AI to automate and improve valuation decisions at scale.

Key Responsibilities

  • Design and iterate models that estimate fair market value, recovery potential, and optimal disposition strategy.
  • Leverage agentic AI to automate cataloging and appraisal workflows.
  • Support Sales to underwrite buyout offers and predict consignment recovery and capacity utilization.
  • Build feedback loops for continuous price optimization and model refinement.
  • Collaborate with Product and Ops to turn insights into high-impact pricing and sourcing actions.

Requirements

What You Bring

  • 4-8+ years in data science, analytics, or preferably pricing within marketplaces, logistics, or asset-heavy environments.
  • Strong modeling and experimentation skills (Python, SQL, Bayesian / probabilistic modeling).
  • Comfort operating with limited data - capable of building and validating proxy-based models.
  • Blend of technical depth and business intuition; fluent in assumptions, risk, and real-world tradeoffs.
  • Curiosity for manufacturing, recommerce, and the circular economy.

Benefits

  • Early-hire equity
  • Best Medical / Dental / Vision plans
  • Parental benefits
  • Flexible PTO
  • Various stipends
  • Clear work-life boundaries

Apply