Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Senior Applied Scientist, Credit Risk
New York, NY · Remote
$165K - $228K/yr
Apply methods from machine learning, statistics, causal inference, optimization, and economics to solve core business problems * Generate and communicate data-driven insights that influence product ...
Quick apply
Senior Applied Scientist, Credit Risk
New York, NY · Remote
$165K - $228K/yr
Apply methods from machine learning, statistics, causal inference, optimization, and economics to solve core business problems * Generate and communicate data-driven insights that influence product ...
Senior Applied Scientist, Credit Risk
New York, NY · On-site
$165K - $228K/yr
Apply methods from machine learning, statistics, causal inference, optimization, and economics to solve core business problems * Generate and communicate data-driven insights that influence product ...
Senior Applied Scientist, Credit Risk
New York, NY · On-site
$165K - $228K/yr
Apply methods from machine learning, statistics, causal inference, optimization, and economics to solve core business problems * Generate and communicate data-driven insights that influence product ...
Postdoctoral Fellow-MSH
Manhattan, NY · On-site
$53K - $73K/yr
... machine-learning/deep-learning methodology research with application to biomedical data. • ... and causal-inference methodology research with application to medical/clinical-trial studies. The ...
Postdoctoral Fellow-MSH
Manhattan, NY · On-site
$53K - $73K/yr
... machine-learning/deep-learning methodology research with application to biomedical data. • ... and causal-inference methodology research with application to medical/clinical-trial studies. The ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Postdoctoral Fellow-MSH
$53K - $73K/yr
Scalable machine-learning/deep ... learning methodology research with application to biomedical data. Mediation and causal-inference ...
Postdoctoral Fellow-MSH
$53K - $73K/yr
Scalable machine-learning/deep ... learning methodology research with application to biomedical data. Mediation and causal-inference ...
Postdoctoral Fellow-MSH
Manhattan, NY · On-site
$53K - $73K/yr
... machine-learning/deep-learning methodology research with application to biomedical data. • ... and causal-inference methodology research with application to medical/clinical-trial studies. The ...
Postdoctoral Fellow-MSH
Manhattan, NY · On-site
$53K - $73K/yr
... machine-learning/deep-learning methodology research with application to biomedical data. • ... and causal-inference methodology research with application to medical/clinical-trial studies. The ...
Postdoctoral Fellow-MSH
$53K - $73K/yr
Scalable machine-learning/deep ... learning methodology research with application to biomedical data. Mediation and causal-inference ...
Postdoctoral Fellow-MSH
$53K - $73K/yr
Scalable machine-learning/deep ... learning methodology research with application to biomedical data. Mediation and causal-inference ...
Senior Research Scientist, Ad Measurements Science
New York, NY · On-site
$107K - $137K/yr
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Senior Research Scientist, Ad Measurements Science
New York, NY · On-site
$107K - $137K/yr
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
The right candidate combines deep technical skills in machine learning and causal inference with genuine curiosity about customer behavior and retail dynamics. They thrive in ambiguity, move fluidly ...
The right candidate combines deep technical skills in machine learning and causal inference with genuine curiosity about customer behavior and retail dynamics. They thrive in ambiguity, move fluidly ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Senior Research Scientist, Ad Measurements Science
New York, NY · On-site
$107K - $137K/yr
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Senior Research Scientist, Ad Measurements Science
New York, NY · On-site
$107K - $137K/yr
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Senior Member of Research Staff, Voleon Securities
New York, NY · On-site
$275K - $300K/yr
... learning, and causal inference. Your colleagues will include internationally recognized experts in artificial intelligence and machine learning research as well as highly experienced finance and ...
Senior Member of Research Staff, Voleon Securities
New York, NY · On-site
$275K - $300K/yr
... learning, and causal inference. Your colleagues will include internationally recognized experts in artificial intelligence and machine learning research as well as highly experienced finance and ...
Causal Inference Machine Learning Postdoctoral information
See Newark, NJ salary details
$37.1K - $39.5K
6% of jobs
$39.5K - $42K
0% of jobs
$42K - $44.4K
0% of jobs
$44.4K - $46.8K
0% of jobs
$46.8K - $49.2K
1% of jobs
$49.2K - $51.7K
4% of jobs
$51.7K - $54.1K
9% of jobs
$55K is the 25th percentile. Wages below this are outliers.
$54.1K - $56.5K
11% of jobs
The median wage is $57.5K / yr.
$56.5K - $58.9K
42% of jobs
$59.1K is the 75th percentile. Wages above this are outliers.
$58.9K - $61.4K
21% of jobs
$61.4K - $63.8K
5% of jobs
$37.1K
$56.7K
$63.8K
How much do causal inference machine learning postdoctoral jobs pay per year?
As of Jul 14, 2026, the average yearly pay for causal inference machine learning postdoctoral in Newark, NJ is $56,702.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,900.00 and $59,100.00 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?
| Aspect | Causal Inference Machine Learning Postdoctoral | Data Scientist |
|---|---|---|
| Required Credentials | PhD in statistics, machine learning, or related field | Bachelor's or Master's in data science, computer science, or related field |
| Work Environment | Academic research, research labs, universities | Corporate, tech companies, startups |
| Industry Usage | Research, academia, specialized industry projects | Business analytics, product development, data-driven decision making |
| Common Search/Comparison | Yes | Yes |
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 job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Newark, NJ look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Newark, NJ are:
What cities near Newark, NJ are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities near Newark, NJ with the most Causal Inference Machine Learning Postdoctoral job openings:
Full-time
Medical, Dental, Vision, Life, Retirement, PTO
Posted 5 days ago
Amazon rating
7.4
Based on 6,965 frontline employees who took The Breakroom Quiz
6th of 39 rated national retailers
Job description
The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision.
As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics.
Key job responsibilities
* Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies.
* Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions.
* Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales.
* Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization.
* Translate complex scientific challenges into clear and impactful solutions for business stakeholders.
* Mentor and guide junior scientists, fostering a collaborative and high-performing team culture.
* Foster collaborations between scientists to move faster, with broader impact.
* Regularly engage with the broader scientific community with presentations, publications, and patents.
A day in the life
You will solve real-world problems by getting and analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication.
Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ
About the team
We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
BASIC QUALIFICATIONS
- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, NY, New York - 183,800.00 - 248,700.00 USD annually
As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics.
Key job responsibilities
* Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies.
* Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions.
* Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales.
* Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization.
* Translate complex scientific challenges into clear and impactful solutions for business stakeholders.
* Mentor and guide junior scientists, fostering a collaborative and high-performing team culture.
* Foster collaborations between scientists to move faster, with broader impact.
* Regularly engage with the broader scientific community with presentations, publications, and patents.
A day in the life
You will solve real-world problems by getting and analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication.
Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ
About the team
We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
BASIC QUALIFICATIONS
- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, NY, New York - 183,800.00 - 248,700.00 USD annually
About Amazon
Sourced by ZipRecruiter
Amazon.com, Inc., commonly known as Amazon, is an American multinational technology company. It was founded by Jeff Bezos in 1994 and initially started as an online marketplace for books. Since then, Amazon has expanded its operations and become one of the largest e-commerce companies in the world. Amazon's primary business is its online retail platform, where customers can purchase a vast array of products, including electronics, clothing, books, home goods, and much more. The company offers a convenient and user-friendly shopping experience, with features such as fast shipping, customer reviews, and personalized recommendations. In addition to its e-commerce platform, Amazon has diversified its business into various other areas. One of its notable ventures is Amazon Web Services (AWS), a comprehensive cloud computing platform that provides services such as storage, compute power, and database management to individuals and businesses. AWS has become a leader in the cloud computing industry, powering many websites and applications worldwide. Amazon has also developed its own consumer electronics, including the popular Amazon Kindle e-reader, Fire tablets, Fire TV streaming devices, and the Alexa-powered Echo smart speakers. The Alexa voice assistant, integrated into these devices, allows users to interact with their devices using voice commands, perform tasks, and access information. Furthermore, Amazon has expanded into media and entertainment. It operates Prime Video, a streaming service that offers a wide range of movies, TV shows, and original content. Amazon Music provides a platform for streaming and purchasing digital music, while Audible offers audiobooks and other audio content. The company's commitment to customer satisfaction and convenience is demonstrated by its membership program, Amazon Prime. Prime members receive various benefits, including free two-day shipping, access to streaming services, exclusive deals, and more.
Industry
It services, book publishers, retail, real estate and computer and electronic product manufacturing
Company size
10,000+ Employees
Headquarters location
Seattle, WA, US