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Causal Inference Jobs in New Jersey (NOW HIRING)

Walmart

(USA) Principal, Data Scientist

Hoboken, NJ · On-site

$132K - $264K/yr

We are seeking a Principal Data Scientist to solve ambiguous, high-value retail problems through rigorous analytics, ML modeling, experimentation, causal inference, and deployment of scalable ...

Rutgers University

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 ...

Jj

Director HTA Statistics & RWE Methods

Raritan, NJ · On-site

$164 - $282.90/hr

Lead the design of advanced study designs and statistical analyses aligned with Payer/HTA expectations (e.g., indirect treatment comparisons, causal inference methods, surrogate endpoint validations ...

Jj

Director HTA Statistics & RWE Methods

Raritan, NJ · On-site

Lead the design of advanced study designs and statistical analyses aligned with Payer/HTA expectations (e.g., indirect treatment comparisons, causal inference methods, surrogate endpoint validations ...

Walmart

(USA) Principal, Data Scientist

Hoboken, NJ · On-site

Strong experience in machine learning: classification models, regression models, NLP, forecasting, unsupervised models, optimization, graph ML, causal inference, causal ML, statistical learning ...

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Causal Inference information

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$55.8K

$100.7K

$137.6K

How much do causal inference jobs pay per year?

As of Aug 1, 2026, the average yearly pay for causal inference in New Jersey is $100,743.00, according to ZipRecruiter salary data. Most workers in this role earn between $87,300.00 and $110,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Causal Inference position, and why are they important?

Success in a Causal Inference role requires strong statistical knowledge, expertise in experimental and quasi-experimental methodologies, and advanced proficiency in programming languages like R or Python, typically acquired with an advanced degree in statistics, economics, data science, or a related field. Familiarity with specialized statistical software (such as Stata, SAS, or causal inference packages in R/Python), as well as experience with large datasets and machine learning tools, is highly valued. Excellent problem-solving abilities, clear communication, and collaboration skills are essential soft skills for effectively conveying complex findings to diverse teams. These competencies are critical to producing reliable insights that guide evidence-based decision-making in business, healthcare, or policy settings.

What are some common challenges faced in a Causal Inference position?

Professionals in Causal Inference often encounter challenges such as dealing with confounding factors, addressing selection bias, and ensuring the validity of assumptions behind statistical models. They must carefully design experiments or leverage observational data while staying vigilant about potential data quality issues and model limitations. Collaboration with subject matter experts, data engineers, and business stakeholders is common to ensure accurate contextualization of results. Overcoming these challenges requires a mix of technical acumen and strong communication skills to translate complex analyses into actionable recommendations.

What is a Causal Inference job?

A Causal Inference job involves using statistical and computational methods to determine cause-and-effect relationships from data. Professionals in this field work with observational and experimental data to identify causal impacts, often in domains like economics, healthcare, social sciences, and technology. They apply techniques such as propensity score matching, instrumental variables, and difference-in-differences to ensure rigorous analysis. These roles are commonly found in academia, policy research, and data science teams within tech and finance companies. Strong skills in statistics, programming (e.g., Python, R), and experimental design are typically required.

What are the most commonly searched types of Causal Inference jobs in New Jersey? The most popular types of Causal Inference jobs in New Jersey are:
What are popular job titles related to Causal Inference jobs in New Jersey? For Causal Inference jobs in New Jersey, the most frequently searched job titles are:
What job categories do people searching Causal Inference jobs in New Jersey look for? The top searched job categories for Causal Inference jobs in New Jersey are:
Causal Inference jobs near you
Infographic showing various Causal Inference job openings in New Jersey as of July 2026, with employment types broken down into 87% Full Time, 10% Part Time, and 3% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $100,743 per year, or $48.4 per hour.

Applied AI/ML & Causal Inference - Senior Associate

JPMorgan Chase & Co.

Jersey City, NJ • On-site

$128K - $195K/yr

Full-time

Medical, Retirement

This job post has expired 1 day ago. Applications are no longer accepted.

JPMorgan Chase & Co. rating

8.0

Company rating: 8.0 out of 10

Based on 492 frontline employees who took The Breakroom Quiz

70th of 170 rated banks

Job description


As a Senior Applied AI/ML Associate within the Global Private Bank, you will own the full lifecycle of high-impact causal and predictive models serving clients across wealth management, deposit, lending, and advisory - from problem framing with business stakeholders through production deployment at scale. You will tackle some of the most data-rich, complex client problems in financial services, where rigorous causal reasoning - not just predictive accuracy - drives the decisions that matter.
Job 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.

Required Qualifications, Capabilities, and Skills
  • Master's and 2+ years of hands on Machine Learning experience or fresh PhD grads in Computer Science, Statistics, Economics, Applied Math, Data Science, or a related quantitative field.
  • 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 Qualifications, Capabilities, and Skills
  • 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.

About Us
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
About the Team
J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.

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