... causal ML, double machine learning, and agentic/LLM systems; translate promising work into ... causal inference. • Deep expertise in causal inference methods: potential outcomes framework ...
... causal ML, double machine learning, and agentic/LLM systems; translate promising work into ... causal inference. • Deep expertise in causal inference methods: potential outcomes framework ...
Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ... Deep expertise in causal inference methods: potential outcomes framework, propensity score methods ...
Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ... Deep expertise in causal inference methods: potential outcomes framework, propensity score methods ...
Applied AI/ML & Causal Inference - Senior Associate
Jersey City, NJ · On-site
$128K - $195K/yr
Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ... Deep expertise in causal inference methods: potential outcomes framework, propensity score methods ...
Applied AI/ML & Causal Inference - Senior Associate
Jersey City, NJ · On-site
$128K - $195K/yr
Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ... Deep expertise in causal inference methods: potential outcomes framework, propensity score methods ...
Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ... Deep expertise in causal inference methods: potential outcomes framework, propensity score methods ...
Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ... Deep expertise in causal inference methods: potential outcomes framework, propensity score methods ...
Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ... Deep expertise in causal inference methods: potential outcomes framework, propensity score methods ...
Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ... Deep expertise in causal inference methods: potential outcomes framework, propensity score methods ...
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous ... Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ...
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous ... Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ...
Postdoctoral Research Associate
Princeton, NJ · On-site
$85K/yr
Postdoctoral research associates work under the direct supervision of the Director for Research and ... causal inference and statistics; computer vision and novel applications of machine learning.
Postdoctoral Research Associate
Princeton, NJ · On-site
$85K/yr
Postdoctoral research associates work under the direct supervision of the Director for Research and ... causal inference and statistics; computer vision and novel applications of machine learning.
Applied Artificial Intelligence/ Machine Learning Lead - Vice President
Jersey City, NJ · On-site
$164K - $260K/yr
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous ... Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ...
Applied Artificial Intelligence/ Machine Learning Lead - Vice President
Jersey City, NJ · On-site
$164K - $260K/yr
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous ... Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ...
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous ... Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ...
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous ... Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate ...
(USA) Principal, Data Scientist
$132K - $264K/yr
This team leads advancements in generative AI, agentic intelligence, machine learning, measurement, and causal inference to redefine retail experiences, optimize operations, and develop new business ...
(USA) Principal, Data Scientist
$132K - $264K/yr
This team leads advancements in generative AI, agentic intelligence, machine learning, measurement, and causal inference to redefine retail experiences, optimize operations, and develop new business ...
Postdoctoral Associate (Faculty)
Newark, NJ · On-site
$65K/yr
Experience with place-based research, causal inference, and program evaluation involving community ... The Postdoctoral Associate will work on projects involving longitudinal crime data and other ...
New
Postdoctoral Associate (Faculty)
Newark, NJ · On-site
$65K/yr
Experience with place-based research, causal inference, and program evaluation involving community ... The Postdoctoral Associate will work on projects involving longitudinal crime data and other ...
New
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ...
Post-Doctoral Associate
Piscataway, NJ · On-site
$63K/yr
Expertise in causal inference and machine learning (in particular reinforcement learning), and strong experience with programming are desired.. Excellent communication and writing skills are needed.s ...
Post-Doctoral Associate
Piscataway, NJ · On-site
$63K/yr
Expertise in causal inference and machine learning (in particular reinforcement learning), and strong experience with programming are desired.. Excellent communication and writing skills are needed.s ...
Postdoctoral Associate for the Center for Climate, Health and Healthcare (CCHH)
New Brunswick, NJ · On-site
$64K/yr
Position Details Position Information Recruitment/Posting Title Postdoctoral Associate for the ... and causal inference methods. * Understanding of HIPAA regulations and/or Human Subjects ...
Postdoctoral Associate for the Center for Climate, Health and Healthcare (CCHH)
New Brunswick, NJ · On-site
$64K/yr
Position Details Position Information Recruitment/Posting Title Postdoctoral Associate for the ... and causal inference methods. * Understanding of HIPAA regulations and/or Human Subjects ...
Causal Inference Machine Learning Postdoctoral information
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 are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in New Jersey? For Causal Inference Machine Learning Postdoctoral jobs in New Jersey, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in New Jersey look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in New Jersey are:
What cities in New Jersey are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities in New Jersey with the most Causal Inference Machine Learning Postdoctoral job openings:
Full-time
Posted 14 days ago
Job description
Job Summary:
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers and businesses. As a Senior Applied AI/ML Associate, you will own the full lifecycle of high-impact causal and predictive models within the Global Private Bank, tackling complex client problems in financial services through rigorous causal reasoning and model deployment.
Responsibilities:
• Frame ambiguous client and operational questions as causal problems — distinguishing prediction from intervention, identifying confounders, and designing the right estimand with Private Bank business leads.
• Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust estimation), experimentation, and classical/generative ML where appropriate.
• Own model quality, identification assumptions, sensitivity analysis, evaluation frameworks, monitoring, and post-deployment iteration.
• Drive productionization and MLOps practices in collaboration with engineering across distributed data infrastructure.
• Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate promising work into production-ready solutions.
• Partner with the broader JPMorganChase AI/ML community, model risk, compliance, and peer LOBs to align on standards and amplify firm-wide impact.
Qualifications:
Required:
• Master's or PhD in Computer Science, Statistics, Economics, Applied Math, Data Science, or a related quantitative field.
• 3+ years of hands on Machine Learning experience in production environments, with a substantial portion focused on causal inference.
• Deep expertise in causal inference methods: potential outcomes framework, propensity score methods, instrumental variables, difference-in-differences, regression discontinuity, synthetic controls, doubly robust and double/debiased ML estimators, and uplift / heterogeneous treatment effect modeling.
• Demonstrated experience designing and analyzing experiments (A/B tests, switchback, quasi-experiments) and reasoning carefully from observational data when experimentation is infeasible.
• Hands-on experience with LLMs and agentic AI — fine-tuning, RAG pipelines, prompt engineering, and the design and deployment of multi-step / tool-using agents in production.
• Strong Python skills; proficiency with causal libraries (DoWhy, EconML, CausalML) alongside PyTorch, scikit-learn, and modern LLM/agent frameworks.
• Experience with large-scale data processing: Spark, Hive, SQL.
• Proven ability to communicate causal assumptions, limitations, and findings to non-technical stakeholders.
Preferred:
• Financial services experience — wealth management, lending, or advisory.
• Bayesian and hierarchical modeling; structural causal models; sequential decision-making / contextual bandits.
• Experience applying causal reasoning to LLM and agent evaluation — counterfactual eval, off-policy estimation, or treatment-effect framing of agent interventions.
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers and businesses. As a Senior Applied AI/ML Associate, you will own the full lifecycle of high-impact causal and predictive models within the Global Private Bank, tackling complex client problems in financial services through rigorous causal reasoning and model deployment.
Responsibilities:
• Frame ambiguous client and operational questions as causal problems — distinguishing prediction from intervention, identifying confounders, and designing the right estimand with Private Bank business leads.
• Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust estimation), experimentation, and classical/generative ML where appropriate.
• Own model quality, identification assumptions, sensitivity analysis, evaluation frameworks, monitoring, and post-deployment iteration.
• Drive productionization and MLOps practices in collaboration with engineering across distributed data infrastructure.
• Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate promising work into production-ready solutions.
• Partner with the broader JPMorganChase AI/ML community, model risk, compliance, and peer LOBs to align on standards and amplify firm-wide impact.
Qualifications:
Required:
• Master's or PhD in Computer Science, Statistics, Economics, Applied Math, Data Science, or a related quantitative field.
• 3+ years of hands on Machine Learning experience in production environments, with a substantial portion focused on causal inference.
• Deep expertise in causal inference methods: potential outcomes framework, propensity score methods, instrumental variables, difference-in-differences, regression discontinuity, synthetic controls, doubly robust and double/debiased ML estimators, and uplift / heterogeneous treatment effect modeling.
• Demonstrated experience designing and analyzing experiments (A/B tests, switchback, quasi-experiments) and reasoning carefully from observational data when experimentation is infeasible.
• Hands-on experience with LLMs and agentic AI — fine-tuning, RAG pipelines, prompt engineering, and the design and deployment of multi-step / tool-using agents in production.
• Strong Python skills; proficiency with causal libraries (DoWhy, EconML, CausalML) alongside PyTorch, scikit-learn, and modern LLM/agent frameworks.
• Experience with large-scale data processing: Spark, Hive, SQL.
• Proven ability to communicate causal assumptions, limitations, and findings to non-technical stakeholders.
Preferred:
• Financial services experience — wealth management, lending, or advisory.
• Bayesian and hierarchical modeling; structural causal models; sequential decision-making / contextual bandits.
• Experience applying causal reasoning to LLM and agent evaluation — counterfactual eval, off-policy estimation, or treatment-effect framing of agent interventions.
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.