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Causal Inference Machine Learning Postdoctoral Jobs

UMass Med School

Post Doc - Open Rank

Worcester, MA · On-site

$75K/yr

Additional Information Postdoc in Causal Inference of Complex Gene Networks We invite applications ... techniques from machine learning, causal inference, statistics, and algorithms . No prior ...

Netflix

OR · On-site

$466K - $750K/yr

Data Science and Engineering ('DSE') at Netflix is aimed at using data, analytics, causal inference, machine learning (ML), and sciences to improve various aspects of our business. The AI initiative ...

Netflix

Machine Learning Scientist 5 - Games

$466K - $750K/yr

Data Science and Engineering ('DSE') at Netflix is aimed at using data, analytics, causal inference, machine learning (ML), and sciences to improve various aspects of our business. The AI initiative ...

Netflix

Machine Learning Scientist 5 - Games

$466K - $750K/yr

Data Science and Engineering ('DSE') at Netflix is aimed at using data, analytics, causal inference, machine learning (ML), and sciences to improve various aspects of our business. The AI initiative ...

Spotter

Machine Learning Scientist

Culver City, CA · Remote

Applying offline policy evaluation, counterfactual evaluation, causal inference, or related ... Experience training, evaluating, tuning, and deploying machine learning models across deep learning ...

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How much do causal inference machine learning postdoctoral jobs pay per year?

As of Jul 14, 2026, the average yearly pay for causal inference machine learning postdoctoral in the United States is $54,223.00, according to ZipRecruiter salary data. Most workers in this role earn between $53,500.00 and $56,500.00 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, 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?

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.

More about Causal Inference Machine Learning Postdoctoral jobs
What cities are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities with the most Causal Inference Machine Learning Postdoctoral job openings:
What states have the most Causal Inference Machine Learning Postdoctoral jobs? States with the most job openings for Causal Inference Machine Learning Postdoctoral jobs include:
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Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in the United States as of July 2026, with employment types broken down into 4% Locum Tenens, 84% Full Time, 11% Part Time, and 1% Contract. Highlights an 84% Physical, 2% Hybrid, and 14% Remote job distribution, with an average salary of $54,223 per year, or $26.1 per hour.
Applied AI/ML & Causal Inference - Senior Associate

Applied AI/ML & Causal Inference - Senior Associate

J.P. Morgan

Jersey City, NJ

Full-time

Medical, Retirement

Posted 12 days ago

Job description

hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role.

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 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 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.â

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