We are looking for a Data Scientist who brings strong fundamentals in machine learning, causal inference, and statistical modeling to solve real advertiser problems. You will build predictive models ...
We are looking for a Data Scientist who brings strong fundamentals in machine learning, causal inference, and statistical modeling to solve real advertiser problems. You will build predictive models ...
We are looking for a Data Scientist who brings strong fundamentals in machine learning, causal inference, and statistical modeling to solve real advertiser problems. You will build predictive models ...
We are looking for a Data Scientist who brings strong fundamentals in machine learning, causal inference, and statistical modeling to solve real advertiser problems. You will build predictive models ...
... Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to ...
... Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to ...
... Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to ...
... Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to ...
... Machine Learning Engineer, you're a player-coach: a strong hands-on contributor who also sets ... inference, hypothesis testing, causal / incrementality measurement). Build scalable tools that ...
... Machine Learning Engineer, you're a player-coach: a strong hands-on contributor who also sets ... inference, hypothesis testing, causal / incrementality measurement). Build scalable tools that ...
Member of Technical Staff, Causality
New York, NY · On-site
$100K - $300K/yr
Design and implement novel causal inference methods for treatment effect modeling. * Translate machine learning papers into production-ready code. * Build robust model evaluation frameworks.
Member of Technical Staff, Causality
New York, NY · On-site
$100K - $300K/yr
Design and implement novel causal inference methods for treatment effect modeling. * Translate machine learning papers into production-ready code. * Build robust model evaluation frameworks.
Senior Manager, Applied Science, Prime Video Advertising
New York, NY · On-site
$164K/yr
... machine learning, generative AI, causal inference, and advertising technology at Internet scale.
Senior Manager, Applied Science, Prime Video Advertising
New York, NY · On-site
$164K/yr
... machine learning, generative AI, causal inference, and advertising technology at Internet scale.
Senior Manager, Applied Science, Prime Video Advertising
New York, NY · On-site
$164K/yr
... machine learning, generative AI, causal inference, and advertising technology at Internet scale.
Senior Manager, Applied Science, Prime Video Advertising
New York, NY · On-site
$164K/yr
... machine learning, generative AI, causal inference, and advertising technology at Internet scale.
Quantitative Researcher, Central Execution Desk
New York, NY · On-site
$120K - $200K/yr
... machine learning. Our ongoing investment in top engineering talent and technology ensures our ... Working on causal inference methods * Developing optimization models for various execution ...
Quantitative Researcher, Central Execution Desk
New York, NY · On-site
$120K - $200K/yr
... machine learning. Our ongoing investment in top engineering talent and technology ensures our ... Working on causal inference methods * Developing optimization models for various execution ...
... inference, hypothesis testing, causal / incrementality measurement). • Build scalable tools that ... Software Engineering, Machine Learning, and/or Data Science. • Strong general software ...
... inference, hypothesis testing, causal / incrementality measurement). • Build scalable tools that ... Software Engineering, Machine Learning, and/or Data Science. • Strong general software ...
Quantitative Researcher, Central Execution Desk
New York, NY · On-site
$120K - $200K/yr
... machine learning. Our ongoing investment in top engineering talent and technology ensures our ... Working on causal inference methods * Developing optimization models for various execution ...
Quantitative Researcher, Central Execution Desk
New York, NY · On-site
$120K - $200K/yr
... machine learning. Our ongoing investment in top engineering talent and technology ensures our ... Working on causal inference methods * Developing optimization models for various execution ...
Staff Machine Learning Engineer - New York
New York, NY · On-site
$250K - $270K/yr
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 ...
Staff Machine Learning Engineer - New York
New York, NY · On-site
$250K - $270K/yr
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 ...
Research Engineer, Machine Learning
New York, NY · On-site
$120K - $180K/yr
Causal inference * Program synthesis and analysis * ML Ops and systems engineering These areas are ... machine learning, computational neuroscience, cognitive science, physics, mathematics.
Research Engineer, Machine Learning
New York, NY · On-site
$120K - $180K/yr
Causal inference * Program synthesis and analysis * ML Ops and systems engineering These areas are ... machine learning, computational neuroscience, cognitive science, physics, mathematics.
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 ...
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 ...
We are looking for a Data Scientist who brings strong fundamentals in machine learning, causal inference, and statistical modeling to solve real advertiser problems. You will build predictive models ...
We are looking for a Data Scientist who brings strong fundamentals in machine learning, causal inference, and statistical modeling to solve real advertiser problems. You will build predictive models ...
Data Scientist III
New York, NY · Remote
Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design ...
Quick apply
Data Scientist III
New York, NY · Remote
Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design ...
Data Scientist III
New York, NY · On-site
Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design ...
Data Scientist III
New York, NY · On-site
Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design ...
Causal Inference Machine Learning Postdoctoral information
See New Brunswick, NJ salary details
$36.6K - $39K
6% of jobs
$39K - $41.4K
0% of jobs
$41.4K - $43.8K
0% of jobs
$43.8K - $46.2K
0% of jobs
$46.2K - $48.6K
1% of jobs
$48.6K - $51K
4% of jobs
$51K - $53.4K
9% of jobs
$54.2K is the 25th percentile. Wages below this are outliers.
$53.4K - $55.8K
11% of jobs
The median wage is $56.8K / yr.
$55.8K - $58.2K
42% of jobs
$58.3K is the 75th percentile. Wages above this are outliers.
$58.2K - $60.6K
21% of jobs
$60.6K - $62.9K
5% of jobs
$36.6K
$56K
$62.9K
How much do causal inference machine learning postdoctoral jobs pay per year?
As of Sep 2, 2026, the average yearly pay for causal inference machine learning postdoctoral in New Brunswick, NJ is $55,956.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,200.00 and $58,300.00 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?
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.
Is it difficult to get a causal inference machine learning postdoctoral position?
Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.
What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in New Brunswick, NJ?
For Causal Inference Machine Learning Postdoctoral jobs in New Brunswick, NJ, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in New Brunswick, NJ look for?
The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in New Brunswick, NJ are:
What cities near New Brunswick, NJ are hiring for Causal Inference Machine Learning Postdoctoral jobs?
Cities near New Brunswick, NJ with the most Causal Inference Machine Learning Postdoctoral job openings:
Full-time
Posted 7 days ago
Amazon rating
7.4
Based on 7,144 frontline employees who took The Breakroom Quiz
5th of 39 rated national retailers
Job description
Amazon Advertising drives billions of ad impressions and millions of clicks daily, powering discovery and sales for advertisers across Amazon's Retail and Marketplace businesses. The Ads Marketing Decision Science team sits at the intersection of data science and marketing strategy. We build intelligent, data-driven systems that analyze advertiser behavior at large scale to deliver the right guidance to the right advertiser at the right time.
Our work spans behavioral modeling, content intelligence, automated decision systems, and GenAI applications, enabling personalized marketing experiences that help advertisers make smarter advertising decisions and grow their business on Amazon.
We are looking for a Data Scientist who brings strong fundamentals in machine learning, causal inference, and statistical modeling to solve real advertiser problems. You will build predictive models, design experiments, develop segmentation frameworks, and leverage GenAI capabilities where applicable, taking solutions end-to-end from proof-of-concept to production at scale. You will partner closely with scientists, engineers, and product managers on a daily basis to prototype rapidly, ensure data integrity in production systems, and deliver measurable advertiser impact
If you are passionate about solving real-world problems with next level science, come join us as we innovate and make history.
Key job responsibilities
Define and execute data science solutions end-to-end, from problem framing through production deployment.
Build machine learning models (classification, regression, clustering, ranking) for advertiser segmentation, propensity modeling, and recommendations.
Apply causal inference and experimentation methods (A/B testing, difference-in-differences, propensity score matching) to measure the impact of marketing interventions.
Analyze large-scale advertiser behavioral data to identify trends, surface growth opportunities, and support optimal decision making.
Collaborate with colleagues across science and engineering disciplines for fast turnaround proof-of-concept prototyping at scale.
Establish and drive data hygiene best practices to ensure coherence and integrity of data feeding into production ML/AI solutions.
Leverage GenAI and LLM capabilities to enhance science products where applicable
A day in the life
You will solve real-world problems by analyzing large volumes of advertiser data, building predictive models, designing experiments, and measuring business impact. You will prototype rapidly, validate ideas with data, and partner with engineers to productize and scale successful solutions. You will collaborate daily with scientists, engineers, and product managers across the advertising organization, working in a cross-functional, fast-paced environment where data drives decisions and helps advertisers grow.
About the team
We are a team of Applied Scientists, Research Scientists, Data Scientists, and Business Intelligence Engineers with deep expertise in ML, NLP, Gen-AI, RL, and causal inference, from a diverse range of backgrounds
We partner closely with strong engineers, product managers, and sales leaders who bring ads-industry depth and experience building scalable modeling and software solutions.
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, computer and electronic product manufacturing and software development
Company size
10,000+ Employees
Headquarters location
Seattle, WA, US