1

Causal Inference Machine Learning Postdoctoral Jobs in Newark, NJ

REMOTE Machine Learning Engineer This project-based consulting role invites an experienced Machine ... Hands-on experience with A/B testing, experimentation design, and causal inference approaches.

Experience defining strategy and technical roadmaps for data science, machine learning, experimentation, or causal inference platforms. Our hybrid model requires 3 days a week in the office. That ...

PhD in Economics, Econometrics, Statistics, or a closely related quantitative field with a strong emphasis on causal inference * 10+ years of experience applying causal inference and machine learning ...

Research Scientist

Manhattan, NY · On-site

$120 - $210/hr

Design and implement novel machine learning models and methods for self-supervised learning, survival analysis, multi-modal learning, causal inference and interpretability. * Translate machine ...

Showing results 21-40

Causal Inference Machine Learning Postdoctoral information

See Newark, NJ salary details

$37.1K

$56.7K

$63.8K

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 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?

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.

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 Newark, NJ?

For Causal Inference Machine Learning Postdoctoral jobs in Newark, NJ, the most frequently searched job titles are:

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:

Senior Manager, Applied Science, Prime Video Advertising

Amazon

New York, NY • On-site

$164K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 23 days ago


Amazon rating

7.4

Company rating: 7.4 out of 10

Based on 7,144 frontline employees who took The Breakroom Quiz

5th of 39 rated national retailers


Job description

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports - including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads.
Are you interested in shaping the future of entertainment and advertising? Prime Video's technology teams are creating best-in-class digital video experiences, and our Advertising Product & Technology organization is at the forefront of revolutionizing the streaming advertising landscape.
The Prime Video Advertising team delivers ad tech solutions that power Prime Video's rapidly growing advertising business across video-on-demand (VOD), live streaming, and display ads-delivering value to both advertisers and viewers worldwide. We focus on critical areas including ad delivery, machine learning-driven optimization, experimentation, audience measurement, and generative AI-powered ad creative solutions.
We are seeking a Senior Manager, Applied Science to lead a team of scientists and engineers building machine learning and AI solutions that directly impact Prime Video's advertising business. In this role, you will own the science strategy and execution for key workstreams including:
- Ad Load Optimization - Balancing advertising revenue with viewer engagement through sophisticated ML models that determine optimal ad frequency, placement, and duration
- Yield Optimization - Maximizing advertising revenue through intelligent allocation, pricing, and forecasting models
- Experimentation & Metrics - Designing and scaling experimentation frameworks and causal inference methods to measure the impact of advertising decisions on both business outcomes and customer experience
- Ad Creative Generation & Augmentation - Leveraging generative AI to create, personalize, and enhance ad creatives at scale
As a leader of leaders, you will set the 3-5 year scientific vision for your organization, build and develop a high-performing team of senior scientists and managers, and drive large-scale ML/AI initiatives that inform strategic decisions for one of the world's largest streaming advertising platforms. You will collaborate closely with engineering, product, and business teams to translate complex scientific capabilities into measurable business impact during a period of rapid growth with a path to $10B in advertising revenue.
This role offers the unique opportunity to shape the science strategy for a new and fast-growing business, working at the intersection of machine learning, generative AI, causal inference, and advertising technology at Internet scale.
BASIC QUALIFICATIONS
- PhD in Computer Science, Computer Engineering, Machine Learning, Statistics, Operations Research, or a related quantitative field
- Experience as a science manager, with demonstrated experience managing other managers or leading science teams through senior leaders
- Extensive track record of solving complex business problems with machine learning, deep learning, and ML engineering at scale
- Proven ability to hire, develop, and manage a high-performing applied science organization, including growing future science leaders
- Experience delivering a scientific vision with a path to execution, including managing strategic research projects spanning multiple years
- Strong technical depth in machine learning with the ability to evaluate and guide work across optimization, causal inference, NLP/generative AI, and recommendation systems
- Experience driving large-scale scientific efforts and making trade-offs between opportunity, resources, and business impact
PREFERRED QUALIFICATIONS
- Experience in advertising technology, ad marketplaces, or revenue optimization domains
- Experience building and deploying large-scale machine learning and AI solutions at Internet scale, particularly in real-time bidding, auction optimization, or content personalization
- Experience with generative AI / large language models applied to creative generation, content augmentation, or personalization
- Track record of managing hybrid science/engineering teams and driving end-to-end ML systems from research to production
- Experience designing and scaling experimentation platforms or causal inference frameworks
- Demonstrated ability to drive 3-5 year strategic initiatives and provide critical inputs into organizational planning (OP1/OP2)
- Active engagement with the external scientific community (publications in ACM, IEEE, NeurIPS, ICML, KDD, or similar venues)
- Proven ability to communicate rigorous scientific concepts and trade-offs to senior leadership and cross-functional stakeholders through verbal and written narratives
- Ability to manage multiple priorities and create a sense of urgency in a fast-paced, high-growth environment
- Experience influencing technical roadmaps and goals across multiple organizations
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 - 240,600.00 - 325,500.00 USD annually
USA, VA, Arlington - 218,800.00 - 295,900.00 USD annually
USA, WA, SEATTLE - 218,800.00 - 295,900.00 USD annually

What Amazon employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Amazon logo

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