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Causal Inference Machine Learning Postdoctoral Jobs in New Brunswick, NJ

OpenX

Data Scientist III

New York, NY

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 ...

MasterCard

Senior Data Scientist

New York, NY · On-site

Design, build, and deploy machine learning models for ad targeting, ranking, and bidding ... Strong background in incrementality measurement, experimentation, A/B testing, causal inference ...

Zocdoc

Director, Marketing Data Science

New York, NY · On-site +1

You follow how the field is evolving (privacy changes, signal loss, new causal inference approaches ... Machine Learning, Artificial Intelligence, Economics, Physics, or a related field is required. A ...

AbbVie

Principal Data Scientist

Florham Park, NJ

Stay current with advances in applied machine learning, causal inference, and pharmaceutical analytics, proactively identifying and piloting emerging methods that could enhance the existing and new ...

AbbVie

Principal Data Scientist

Florham Park, NJ · On-site

$124K/yr

Stay current with advances in applied machine learning, causal inference, and pharmaceutical analytics, proactively identifying and piloting emerging methods that could enhance the existing and new ...

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Causal Inference Machine Learning Postdoctoral information

See New Brunswick, NJ salary details

$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?

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 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:

Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in New Brunswick, NJ as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 23% Part Time, 3% Temporary, and 3% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $55,956 per year, or $26.9 per hour.

Data Scientist III

OpenX

New York, NY

Full-time

Medical, Dental, Vision, Retirement

Re-posted 26 days ago

Job description

Company at a Glance
OpenX is focused on unleashing the full economic potential of digital media companies. We do this by making digital advertising markets and technologies that are designed to deliver optimal value to publishers and advertisers on every ad served across all screens.
At OpenX, we have built a team that is uniquely experienced in designing and operating high-scale ad marketplaces, and we are constantly on the lookout for thoughtful, creative executors who are as fascinated as we are about finding new ways to apply a blend of market design, technical innovation, operational excellence, and empathetic partner service to the frontiers of digital advertising.
A Data Scientist III is a proficient, fully independent scientist who owns medium-to-large data science projects end-to-end from problem formulation and research through to deploying and maintaining production models. In this role, you will build production-ready models and analyses that solve real marketplace problems, partner with product and engineering to ship them, mentor junior scientists, and act as a strong technical voice within your team.
Problems at this level include bidding and yield modeling, relevance and prediction systems at exchange scale, experimentation and causal measurement of marketplace changes, and the feature engineering, validation, and monitoring required to run ML reliably in production.
The ideal candidate brings a solid applied machine learning foundation, growing judgment in selecting methods for business problems at scale, and a track record of carrying analytical work from an ambiguous question through to measurable production impact.
Key Responsibilities:
  • Modeling & Technical Execution:
    • Own the end-to-end data science lifecycle for moderately complex models and significant project components - spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining.
    • 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), selecting appropriate methods for complex data science problems.
    • Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate.
    • Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance.
    • Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance.
  • Execution & Collaboration:
    • Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems.
    • Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions.
    • Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus on actionable insights and business outcomes.
    • Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration.
    • Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation.
  • Mentorship & Influence:
    • Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills.
    • Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well-reasoned arguments and expertise.
    • Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews).
    • Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non-technical audiences.
    • Participate actively in recruiting, providing high-quality, graded interview feedback for candidates up to this level.

Required Qualifications:
  • B.S. or M.S. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or a related technical field with 5+ years of relevant industry experience; OR a Ph.D. in a related field with 2+ years of relevant experience.
  • Demonstrated ability to independently own the full data science lifecycle from problem formulation and feature engineering through model deployment, monitoring, and ongoing maintenance.
  • Solid expertise in several core areas of machine learning and/or statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference, experimentation design), with the judgment to select appropriate methods for complex problems.
  • Strong foundation in probability and statistics, including techniques that scale to large datasets.
  • Experience designing and analyzing experiments (e.g., A/B testing) and building robust model and experiment validation frameworks.
  • Strong Python and SQL skills; experience with ML frameworks such as TensorFlow or PyTorch.
  • Ability to write efficient, modular, well-tested code and to collaborate with engineering to move models and analyses into production.
  • Strong communication skills, including the ability to convey complex technical concepts to both technical and non-technical audiences.

Desired Characteristics:
  • Experience developing, evaluating, or optimizing models or bidding algorithms for RTB environments.
  • Experience working with a cloud platform like GCP/AWS/Azure, with emphasis on GCP and the Vertex AI platform.
  • Experience with ML pipeline and orchestration tools such as TFX, Kubeflow, or Airflow.
    Familiarity with other programming languages such as Java and Go.
  • Experience working in digital media, marketing technology, or advertising technology, especially in marketplace, auction, or exchange systems.
  • Experience supporting and improving production ML models beyond their initial deployment.
  • Experience mentoring junior data scientists.

$143,650 - $160,550 a year
Pursuant to any state, local ordinance, or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.
OpenX is committed to fair and equitable compensation practices. For all applicants, the base salary range is noted above, per year + bonus + equity + benefits. A candidate's salary is determined by various factors including, but not limited to, relevant work experience, skills, and certifications.
A summary of our benefits, which include medical, dental, vision, 401k, equity and more, can be viewed here: https://www.openx.com/company/careers/ A candidate's salary is determined by various factors including, but not limited to, relevant work experience, skills, and certifications.
OpenX VALUES
Our five company values form a solid bedrock serving to define us as a group and guide the company. Our values remind us that how we do things often matters as much as what we do.
WE ARE ONE
We are one team. There are no exceptions. We are a group of strong and diverse individuals unified by a shared mission. We embrace challenges and win together as a team. We respect and care about our colleagues and cultivate an inclusive culture
WE ARE CUSTOMER CENTRIC
We innovate on behalf of our customers. We understand, respect, and listen carefully to our customers. We build great products to solve our customers' problems. We manage our customers' expectations clearly and honestly. We are a trusted partner to all of our customers - we act with integrity at all times. We care.
OPENX IS OURS
We are all owners of OpenX
We all have a voice to improve OpenX
We stake our personal and professional reputations on the excellence of our work
We are not interested in just "doing our jobs"; we take ownership to drive results
WE ARE AN OPEN BOOK
We understand and respect what each of us does. We are eager to teach and share what we know with others, both internally and externally. We are eager to learn from others and we ask questions internally and externally.
WE EVOLVE FAST
We take responsible risks and own and learn from our mistakes. We recognize and repeat success. We actively seek out and provide constructive feedback. We adapt quickly and embrace change. We tackle growth and learning with real urgency. We are endlessly curious.
OpenX TRAITS
Our three traits capture what makes a great team member at OpenX.
HUMBLE
Ideal team players are humble and demonstrate integrity. They put the team's success above their own, share credit generously, and value collective achievements. They are self-assured, open to coaching, and committed to continuous learning.
DRIVEN
Ideal team players are results-driven and motivated. They are curious, always seeking more to do, learn, and take on. As proactive problem-solvers, they take initiative without needing external motivation. They continuously think about the next steps and opportunities for improvement.
SMART
Ideal team players are smart and possess the intellectual acumen to understand the complexities of our organization and industry. They are interpersonally intelligent, good communicators, and exemplify sound judgment in their interactions across the company to foster a collaborative environment.
OpenX is committed to equal employment opportunities.
It is a fundamental principle at OpenX not to discriminate against employees or applicants for employment on any legally-recognized basis including, but not limited to: age, race, creed, color, religion, national origin, sexual orientation, sex, disability, predisposing genetic characteristics, genetic information, military or veteran status, marital status, gender identity/transgender status, pregnancy, childbirth or related medical condition, and other protected characteristic as established by law.
OpenX Applicant Privacy Policy
Applicants can review our Applicant Privacy Policy at any time by visiting the following link: https://www.openx.com/privacy-center/applicant-privacy-policy/.
Effective Date: November 21, 2024

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