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Bayesian Phd Jobs in Seattle, WA (NOW HIRING)

With support from PhD economists, data scientists, and growth experts, Haus' AI-driven technology ... Experience with optimization techniques, including reinforcement learning (RL), Bayesian methods ...

Applied Scientist- Pricing

Seattle, WA ยท On-site

$156K - $335K/yr

Experience with one or more of the following: causal inference, Bayesian modeling, structural ... Advanced degree (MS or PhD preferred) in statistics, mathematics, economics, operations research ...

Applied Scientist- Pricing

Seattle, WA ยท On-site

$156.80 - $335/hr

Experience with one or more of the following: causal inference, Bayesian modeling, structural ... Advanced degree (MS or PhD preferred) in statistics, mathematics, economics, operations research ...

Applied Science Manager, Stores-Ads Science

Seattle, WA ยท On-site

$43K - $59K/yr

... PhD, or Master's degree and 6+ years of applied research experience - Knowledge of ML, NLP ... Bayesian network, potential outcomes, A/B testing, experiments, quasi-experiments, and data science ...

Senior Motion Planning Engineer

Seattle, WA ยท On-site +1

$172K - $229K/yr

PhD preferred in Robotics, Computer Science, Computer Engineering, Mechanical Engineering, or a ... Experience with Bayesian modeling and inference techniques for decision making under uncertainty.

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Showing results 21-39

Bayesian Phd information

What is a Bayesian PhD?

A Bayesian PhD typically refers to an individual who has completed a doctoral program with a focus on Bayesian statistics or Bayesian methods in their research. Bayesian statistics is a branch of statistics that uses probability distributions to represent uncertainty about unknowns, updating beliefs as new data becomes available. Students in this field learn to develop and apply Bayesian models to a wide range of problems in science, engineering, and social sciences. A PhD program with a Bayesian focus often involves advanced coursework in probability theory, statistical inference, and computational methods, as well as original research using Bayesian approaches.

What are the key skills and qualifications needed to thrive as a Bayesian PhD?

To thrive as a Bayesian PhD, you need advanced knowledge of probability theory, statistical inference, and mathematics, typically supported by a doctoral degree in statistics, mathematics, or a related field. Proficiency with statistical programming languages like R, Python, and specialized Bayesian tools such as Stan or BUGS is essential. Strong critical thinking, problem-solving, and clear communication skills help in articulating complex analyses and collaborating across disciplines. These capabilities are crucial for developing rigorous models, conducting impactful research, and translating statistical insights into actionable solutions.

What are some common challenges faced by a Bayesian PhD researcher during collaborative projects?

Bayesian PhD researchers often collaborate with interdisciplinary teams, which can present challenges such as communicating complex statistical concepts to non-specialists and integrating Bayesian methods with other analytical frameworks. Balancing the depth of theoretical work with practical problem-solving, managing computational demands, and aligning project goals with collaborators' expectations are also common hurdles. Successful collaboration typically requires strong communication skills, adaptability, and a willingness to bridge methodological gaps between disciplines.

What is the difference between Bayesian Phd vs Data Scientist?

AspectBayesian PhdData Scientist
Required CredentialsPhD in Statistics, Mathematics, or related fieldBachelor's or Master's in Data Science, Statistics, or related field
Work EnvironmentResearch-focused, academic or specialized industry rolesBusiness-focused, tech companies, or consulting firms
Industry UsageAcademic research, advanced analytics, specialized modelingData analysis, machine learning, business insights
Common Search/ComparisonYesYes

While a Bayesian PhD specializes in advanced statistical modeling and research, a Data Scientist applies data analysis and machine learning techniques in practical business contexts. Both roles require strong analytical skills, but the Bayesian PhD typically focuses on theoretical development, whereas the Data Scientist emphasizes application and implementation.

Infographic showing various Bayesian Phd job openings in Seattle, WA as of August 2026, with employment types broken down into 84% Full Time, 13% Part Time, and 3% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution.

Staff Machine Learning Engineer - Seattle

Haus Analytics

Seattle, WA โ€ข On-site

$180 - $240/hr

Other

Medical, Dental, Vision, Life, PTO

Posted 10 days ago


Job description

About Haus

Haus is the causal marketing platform top businesses trust to optimize billions in ad spend worldwide. With support from PhD economists, data scientists, and growth experts, Hausโ€™ AI-driven technology translates complex marketing measurement into clear action and outcomes, enabling brands like Dyson, Wayfair, Sonos, Fanduel, SharkNinja, and Intuit to optimize spend, accelerate growth, and make smarter marketing decisions at scale.

The Role

This role will drive high-impact projects for advanced marketing planning, analysis, and optimization at Haus using optimization, machine learning, and causal inference. We are looking for individuals who not only excel in problem solving and critical thinking, but also are interested and proficient in writing production code, turning ideas to scalable systems. This role specifically will work deeply on the cMMM machine learning problem space. The role will be a blend of working with applied scientists, data scientists, data engineers and other MLEs to deliver trustworthy results to our customers while focusing on creating processes that help scale the business.

What you'll do
  • Drive initiatives from concept to final product delivery, ensuring seamless end-to-end execution: lead or contribute to the design, development, optimization, and product ionization of machine learning (ML) solutions for complex and high-impact problems.

  • Able to implement probabilistic techniques into reusable statistical libraries, including bootstrapping, statistical tests, and ML models/regressions.

  • Build and maintain the ML systems that power Hausโ€™ product lines (specifically cMMM).

  • Review code and designs of teammates, providing constructive feedback.

  • Lead and collaborate with engineering and cross-functional partners across product, engineering, and science teams to drive system development from ideation to production.

  • Drive design and implementation of AI (Agentic) workflows for ML pipelines (including model validation)

  • Mentor ML engineers and raise the organizationโ€™s ML bar

Qualifications
  • PhD or equivalent experience in Computer Science, Engineering, Mathematics or related field

  • 10+ years of industry experience ideally with a focus on Machine Learning Engineer, building and operating production ML systems.

  • Experience in exploratory data analysis, statistical modeling, hypothesis testing, and experimental design.

  • Experience working with cross-functional teams (product, science, product ops etc).

  • Proficiency in one or more object-oriented programming languages (e.g. Python, Go, Java, C++).

Bonus Points
  • Experience in modern deep learning architectures and probabilistic modeling.

  • Expertise in the design and architecture of ML systems and workflows.

  • Experience with optimization techniques, including reinforcement learning (RL), Bayesian methods, and multi-armed bandits.

  • Experience with MLFlow

  • Experience with data science or machine learning approaches in marketing and growth

What We Offer:

We're a high-performance, low-ego team operating in a fast-moving environment. We care deeply about our customers and expect everyone to take full ownership of their work - this is a place where high expectations fuel even higher growth.

If you thrive in ambiguity, take pride in raising the bar, and want to work alongside top-tier peers who challenge and support you, you'll find unmatched opportunities here. If you're looking for predictability or rigid structure or you prefer order-taking to go-getting, we're probably not the right fit - and that's okay.

We work in small, mission-driven teams that prioritize inclusion, collaboration, and growth over hierarchy or red tape.

Some of our benefits include:

  • Flexible PTO - take time when you need it!

  • Equity - Startup environment with part-ownership in our successes

  • Top of the line health, dental, and vision insurance - multiple plan options so you can pick what fits you best

  • WFH stipend to support the set up you need to be productive

  • Events & Offsites - opportunities to connect and celebrate in real life!

  • Free Lunch - Grab a bite on us when you choose to work from the office (hub locations include SF, NYC and Seattle)

  • New Parent Leave - take time to welcome your newest Hausmate

We value in-person collaboration at Haus and give preference to candidates within commuting distance of our offices in San Francisco, Seattle, and New York City.

Haus is an equal opportunity employer. We make hiring decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected status.

We believe diverse perspectives make us stronger and are committed to an inclusive culture where everyone feels seen, heard, and empowered to contribute. Bring your authentic self โ€” we would love to hear from you.

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