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Causal Inference Machine Learning Postdoctoral Jobs in Seattle, WA

Demonstrated track record of setting technical direction for large-scale machine learning or statistical systems that delivered measurable business impact. * Deep expertise in causal inference, data ...

As a Staff Machine Learning Engineer in Remitly's Core AI/ML team, you'll work at the heart of our ... Experience with one or more of Deep Learning algorithms, Large Language Models, Causal Inference ...

Artificial Intelligence Engineer

Bellevue, WA · On-site

$129K - $155K/yr

Lead the development and deployment of machine learning models and analytical solutions for ... Strong knowledge of statistics experimental design and causal inference. * Handson experience with ...

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Causal Inference Machine Learning Postdoctoral information

See Seattle, WA salary details

$40.4K

$61.7K

$69.4K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Jul 15, 2026, the average yearly pay for causal inference machine learning postdoctoral in Seattle, WA is $61,707.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,900.00 and $64,300.00 per year, depending on experience, location, and employer.

What is a Causal Inference Machine Learning Postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a Causal Inference Machine Learning Postdoctoral researcher, and why are they important?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by Causal Inference Machine Learning Postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Seattle, WA? For Causal Inference Machine Learning Postdoctoral jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Seattle, WA look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Seattle, WA are:
Principal Applied Scientist

Principal Applied Scientist

Microsoft

Redmond, WA • On-site

Full-time

Re-posted 4 days ago


Microsoft rating

8.5

Company rating: 8.5 out of 10

Based on 131 frontline employees who took The Breakroom Quiz

68th of 209 rated software companies


Job description

Overview
Our Signals Modeling team builds the intelligence that powers how the advertising marketplace understands user behavior, measures impact and optimizes outcomes from initial impressions through downstream conversions and long-term advertiser value.
We develop large-scale learning systems that infer intent and causal effects from incomplete and noisy feedback, enabling principled decision-making across ranking, bidding, pricing, and budget allocation. Our work sits at the foundation of marketplace optimization, where accurate attribution and measurement directly influence billions in advertising spend.
The team designs and operates state-of-the-art modeling platforms spanning representation learning, weak-supervision, multi-objective training, calibration, and rigorous experimentation. We transform sparse engagement signals into reliable learning targets and build models that remain robust under delayed conversions, selection bias, and rapidly shifting marketplace dynamics.
As a Principal Applied Scientist, you will help define the future of data-driven attribution and causal measurement, shaping the methodologies that determine how value is estimated and optimized across the ecosystem. You will partner across research, engineering, and product leadership to introduce advanced inference techniques into production systems operating at massive scale.
This is a high-ownership role focused on solving structurally hard problems where ground truth is limited, experimentation is non-trivial, and scientific rigor is essential to unlocking durable marketplace advantage.
Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.
Responsibilities
  • Define and drive the scientific and technical strategy for data-driven attribution (DDA) and causal measurement across advertising systems.
  • Establish methodologies for incrementality estimation, counterfactual learning, delayed-feedback modeling, and bias correction in environments with partial observability.
  • Lead the design and production adoption of attribution and causal inference frameworks that improve bidding, ranking, optimization, and advertiser ROI at web scale.
  • Set evaluation standards that distinguish correlation from causation and elevate experimental rigor across teams.
  • Identify capability gaps and introduce advanced research, tools, or modeling approaches to strengthen measurement foundations.
  • Operate across organizational boundaries to align research, engineering, product, and business leaders on measurement strategy.
  • Serve as a subject-matter expert and technical advisor on attribution and causal inference.
  • Mentor scientists and influence technical direction to raise the organization's scientific bar.

Qualifications
Required Qualifications:
  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
    • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)
    • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
    • OR equivalent experience.

Preferred Qualifications:
  • Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)
    • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
    • OR equivalent experience.
  • Demonstrated track record of setting technical direction for large-scale machine learning or statistical systems that delivered measurable business impact.
  • Deep expertise in causal inference, data-driven attribution, treatment effect estimation, counterfactual learning, or experimental design - applied in production environments.
  • Experience leading ambiguous, high-impact initiatives where ground truth is limited and methodological rigor is critical.
  • Proven ability to influence strategy and drive adoption of new measurement or modeling approaches beyond an immediate team.
  • Significant experience developing and deploying production ML systems across multiple stages of the product lifecycle.
  • Solid scientific judgment with a history of selecting appropriate methodologies under real-world constraints.
  • Exceptional communication skills with the ability to translate complex technical concepts into guidance for senior technical and business leaders.
  • Recognized expertise in attribution, incrementality, marketplace experimentation, or causal ML.
  • Track record of driving multi-year research or modeling agendas that materially improved product outcomes.
  • Experience defining measurement strategy for advertising platforms, marketplaces, or large-scale recommendation systems.
  • Publications, patents, or widely adopted internal methodologies in causal inference, experimentation, econometrics, or applied machine learning.
  • History of mentoring senior scientists and elevating organizational scientific capability.
  • Experience influencing director- or VP-level technical strategy.

#MicrosoftAI
Applied Sciences IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

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About Microsoft

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Our infrastructure is comprised of a large global portfolio of more than 100 datacenters and 1 million servers. Our foundation is built upon and managed by a team of subject matter experts working to support services for more than 1 billion customers and 20 million businesses in over 90 countries worldwide. With environmental sustainability and optimization at the forefront of our datacenter design and operations, we continue to grow and evolve as we meet the ever-changing business demands that hold Microsoft as a world-class cloud provider.

Industry

Computer and computer peripheral equipment and software wholesalers

Company size

10,000+ Employees

Headquarters location

Redmond, WA, US

Year founded

1975

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