ZipRecruiter

Log In

1

Probabilistic Modeling Jobs in Austin, TX (NOW HIRING)

Atmosphere TV

Senior Data Scientist

Austin, TX · On-site

Background in Bayesian inference or probabilistic modeling. * Experience launching data products in coordination with Sales, Marketing, or Client Success. * Exposure to computer vision or NLP.

JC CORPORATIONS

AI Architect

Austin, TX · On-site

Demonstrable judgment on deterministic versus probabilistic system design you can point to systems where you deliberately kept the model out of the critical path. RAG and code/knowledge-graph design ...

Jean Martin, Inc

AI Architect (Pod Lead)

Austin, TX · On-site

$54.75 - $75/hr

Demonstrable judgment on deterministic versus probabilistic system design -- you can point to systems where you deliberately kept the model out of the critical path. RAG and code/knowledge-graph ...

Last Energy

Licensing Manager

Austin, TX · On-site

... probabilistic safety analysis (PSA), and licensing roadmap progress. This role will work with ... Support safety analysis with data, modeling, reviews, and documentation * Monitor regulatory ...

Last Energy

Licensing Manager

Austin, TX · On-site

... probabilistic safety analysis (PSA), and licensing roadmap progress. This role will work with ... Support safety analysis with data, modeling, reviews, and documentation * Monitor regulatory ...

Aalo Atomics

PRA Engineer

Austin, TX · On-site

What you'll do * PRA Model Development & Application: * * Develop, update, and maintain ... Minimum of five (5) years of relevant experience in Probabilistic Risk Assessment (PRA) within the ...

Last Energy

Licensing Manager

Austin, TX · On-site

... probabilistic safety analysis (PSA), and licensing roadmap progress. This role will work with ... Support safety analysis with data, modeling, reviews, and documentation * Monitor regulatory ...

Last Energy

Licensing Associate

Austin, TX · On-site

Conduct spreadsheet analysis, including data collection, modeling, and reporting * Research, track ... Strong analytical skills with exposure to probabilistic safety analysis or quantitative risk ...

Last Energy

Licensing Associate

Austin, TX

Conduct spreadsheet analysis, including data collection, modeling, and reporting * Research, track ... Strong analytical skills with exposure to probabilistic safety analysis or quantitative risk ...

Last Energy

Licensing Associate

Austin, TX

Conduct spreadsheet analysis, including data collection, modeling, and reporting * Research, track ... Strong analytical skills with exposure to probabilistic safety analysis or quantitative risk ...

Fynbosys Inc

AI Architect

Austin, TX · On-site

Demonstrable judgment on deterministic versus probabilistic system design -- you can point to * systems where you deliberately kept the model out of the critical path. * RAG and code/knowledge-graph ...

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning in real applications a plus. Experience with Spark, TensorFlow, Keras, and PyTorch a plus

next page

Showing results 1-20

All JobsProbabilistic Modeling JobsProbabilistic Modeling Jobs in Austin, TX

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.
What are popular job titles related to Probabilistic Modeling jobs in Austin, TX? For Probabilistic Modeling jobs in Austin, TX, the most frequently searched job titles are:
What job categories do people searching Probabilistic Modeling jobs in Austin, TX look for? The top searched job categories for Probabilistic Modeling jobs in Austin, TX are:
What cities near Austin, TX are hiring for Probabilistic Modeling jobs? Cities near Austin, TX with the most Probabilistic Modeling job openings:
Probabilistic Modeling jobs near you

Senior Data Scientist

Atmosphere TV

Austin, TX • On-site

Full-time

Vision, Retirement

Posted 18 days ago

Job description

About Atmosphere TV
Atmosphere TV is the leading streaming TV platform built specifically for businesses. Unlike ad networks or signage companies, Atmosphere is the only true TV company whose first priority is to entertain television audiences outside of the home. Our content is designed to be fun, engaging, and worth watching, transforming waiting rooms, gyms, bars, and restaurants into better experiences for customers and better businesses for owners.
We are the first and only company to think about both the business owner and their customers when creating TV content. With 60,000+ venues and a global audience of over 150 million monthly viewers, Atmosphere TV is redefining what TV means outside the living room.
About the role
As we scale both our advertising business and our venue network, we are investing in the causal-inference and predictive-modeling foundation that will make Atmosphere smarter on both sides of the business - how we measure and sell advertising, and how we grow and retain the venues that make up our network. As our first data scientist, you will help shape this function from the ground up.
We are looking for a Senior Data Scientist to serve in a high-impact, high-visibility role that sits at the intersection of data science, sales, product, and go-to-market. The ideal candidate is an excellent applied statistician and modeler: someone who deeply understands causal inference and predictive methods, knows which technique fits which problem and why, and can turn that rigor into products the business actually uses. You will design, build, and ship the models that quantify the real-world impact of campaigns, capture what makes individual venues valuable and what puts them at risk, and turn rigorous methodology into a core competitive advantage for Atmosphere.
What you'll do
Causal Measurement & Incrementality
  • Design and own Atmosphere's causal measurement framework - isolating the incremental impact of exposure on real-world outcomes (foot traffic, store visitation, conversion) from confounders like organic visitation trends, seasonality, and competing media.
  • Build statistically rigorous causal designs: exposed/control group construction, geo-based experiments, difference-in-differences, and synthetic control.
  • Stand up the incrementality capability that underpins how we sell - turning measurement into a differentiator our go-to-market teams can take to market and our clients can trust.

Predictive Venue Modeling & Contextual Enrichment
  • Build models that predict a venue's intrinsic revenue potential, enabling us to prioritize prospective venues for acquisition as well as flag under-monetized venues we already operate.
  • Leverage venue streaming and engagement data to better understand what drives retention vs. churn, helping us create a ranked list of customer challenges to solve from the full list of potential friction points, pain signals, and leading indicators.
  • Model and infer latent venue attributes to sharpen both the segments advertisers target against and the feature sets your own revenue and churn models depend on.

Productization & Go-To-Market
  • Use measurement outputs to generate actionable insights that feed back into campaign strategy - daypart targeting, venue type optimization, creative performance, audience segmentation
  • Develop closed-loop optimization frameworks that make every campaign smarter than the last, building proprietary benchmarks and norms by vertical/category

Qualifications
  • 5+ years of experience in data science, applied research, or quantitative analytics.
  • Deep fluency in causal inference and applied statistics - including experience with A/B testing, geo experiments, difference-in-differences, synthetic control, propensity methods, and regression modeling - and can explain clearly why one fits a given problem better than another.
  • Expertise in predictive modeling (e.g., gradient-boosted trees, survival/churn models, calibration, and honest out-of-sample evaluation)
  • Sound judgment about what makes a model trustworthy: validation, uncertainty, and knowing when a result is solid enough to act on.
  • Strong programming skills in Python and/or R, and comfort with SQL for working with data at scale.
  • A track record of translating complex statistical work into clear, business-friendly outputs, and of shipping models and products that get used
  • Ability to operate in a fast moving environment and balance rigor with pragmatism.

Nice-to-Have:
  • Familiarity with location/mobility data or other behavioral signal data.
  • Familiarity with out-of-home (OOH), digital out-of-home (DOOH), or CTV advertising, or experience at an ad tech company, media platform, or measurement vendor.
  • Exposure to marketing measurement, media effectiveness, mixed media modeling (MMM), or multi-touch attribution (MTA).
  • Background in Bayesian inference or probabilistic modeling.
  • Experience launching data products in coordination with Sales, Marketing, or Client Success.
  • Exposure to computer vision or NLP..

Compensation & Benefits:
  • Competitive salary
  • Company Equity
  • Company 401(k) with employer matching
  • Competitive insurance plans
  • Flexible Time Off Policy

Our Commitment to Diversity:
Don't meet every single requirement? Research shows that women and underrepresented groups often hesitate to apply unless they meet all the criteria. At Atmosphere, we're committed to building a diverse, inclusive team where creativity, innovation, and teamwork thrive. If you're excited about this role but your experience doesn't perfectly align with every qualification, we still encourage you to apply-you might be the right fit for this or another role.

Apply