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Causal Inference Machine Learning Postdoctoral Jobs in Beverly Hills, MO

Pwc

AI Engineer

Saint Louis, MO

$50K - $112K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

Varsity Tutors

LSAT Logical Reasoning Tutor

Saint Louis, MO · Remote

$26 - $40/hr

... inference questions. Ability to explain argument structure, conditional logic, causal reasoning ... Ability to adapt to different learning styles and student needs. Ways To Connect With Students * 1 ...

Saint Louis University

Post Doctoral Fellow

Saint Louis, MO · On-site

$47K - $64K/yr

Postdoctoral Fellow - Computational Biology / Bioinformatics Focus:Multi-omics and Longitudinal ... Develop and apply statistical and machine-learning models(e.g., mixed-effects models, survival ...

Saint Louis University

Post Doctoral Fellow

Saint Louis, MO · On-site

$47K - $64K/yr

Postdoctoral Fellow - Computational Biology / Bioinformatics Focus: Multi-omics and Longitudinal ... Develop and apply statistical and machine-learning models (e.g., mixed-effects models, survival ...

Socket.dev

Lead Service Management Engineer

O Fallon, MO · On-site

$122 - $207/hr

... inference for AI and generative AI systems. * Ensure Data Quality & Governance: Oversee data ... Understanding of data needs for machine learning--dataset preparation, feature/label management ...

New

Merck Group

Staff Software Engineer

Saint Louis, MO · On-site

$110K - $165K/yr

At least 3 years of hands-on experience in machine learning, data science, search relevance, or ... Familiarity with ML model deployment and inference pipelines Location This role can be based in ...

MasterCard

Lead Service Management Engineer

O Fallon, MO · On-site

$95K - $126K/yr

... inference for AI and generative AI systems.* Ensure Data Quality & Governance: Oversee data ... AI/ML Data Lifecycle Knowledge: Understanding of data needs for machine learning-dataset ...

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Beverly Hills, MO

Causal Inference Machine Learning Postdoctoral information

See Beverly Hills, MO salary details

$33.1K

$50.6K

$56.9K

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

As of Aug 21, 2026, the average yearly pay for causal inference machine learning postdoctoral in Beverly Hills, MO is $50,553.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,900.00 and $52,700.00 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.
Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Beverly Hills, MO as of August 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $50,553 per year, or $24.3 per hour.

Postdoctoral Research Associate - Oncology

Washington University

Saint Louis, MO • On-site

Full-time

Re-posted 15 days ago

Job description

Location
ST. LOUIS, MO 63110Position Summary
This posting is for a Postdoctoral Research Associate position in Dr. David Spencer's Lab in the Division of Oncology, Department of Medicine.
Dr. David Spencer is seeking a postdoctoral researcher to lead exciting new projects in the field of Cancer Genetics. The Spencer lab studies human genomics, cancer genetics, gene regulation, and the genomics and epigenetics of acute myeloid leukemia and has published articles in Cancer Cell, Cell, Leukemia, and the New England Journal of Medicine. The lab has extensive expertise in both bench science and bioinformatics and is a key member of the cancer genetics group at WashU and Siteman Cancer Center. Dr. Spencer has received funding from ASH, the Cancer Research Foundation, Doris Duke, Siteman Cancer Research Fund, and the NCI, and has extensive collaborations with the McDonnell Genome Institute.
Position will play a lead role in new studies of epigenetics, DNA methylation, and gene regulation in acute myeloid leukemia using primary human samples, cell lines, and mouse models. Responsibilities will be to design, perform, and analyze data from genomic assays including long read sequencing using the Oxford Nanopore and Pacific Biosciences platforms, single-cell multi-omics, 3D genome architecture studies (e.g., Micro-C and HiC), CUT&RUN and CUT&Tag. Experimental systems will include CRISPR/Cas9-mediated genetic and epigenetic manipulation of AML cells and massively parallel reporter assays to investigate gene regulation in leukemia. Computational analysis of these data will involve applying existing tools and developing new ones, including machine and deep learning methods. There will be opportunities for development of new cell line and animal models and testing, evaluation, and analysis of new genomic technologies. Authorship on high-profile publications resulting from this work is expected.
The postdoc will work directly with Dr. Spencer on a day-to-day basis, allowing for close mentorship to promote career development, including an independent research program and external funding. Further career and professional development training provided through the Career Center, Teaching Center, Office of Postdoctoral Affairs, and campus groups.
Job Description
Primary Duties & Responsibilities:
Information on being a postdoc at WashU in St. Louis can be found at https://postdoc.wustl.edu/prospective-postdocs-2/.
Trains under the supervision of a faculty mentor including (but not limited to):
  • Assists with grant preparation and reporting.
  • Prepares and submits papers on research.
  • Assists in the design of research experiments.
  • Evaluates research findings and assists in the reporting of the results.
  • Conscientious discharge of their research responsibilities.
  • Maintains conformity with ethical standards in research.
  • Maintains compliance with good laboratory practice including the maintenance of adequate research records.
  • Engages in open and timely discussion with their mentor regarding possession or distribution of material, reagents, or records belonging to their laboratory and any proposed disclosure of findings or techniques privately or in publications.
  • Collegial conduct towards co-trainees, staff members and members of the research group.
  • Adherence to all applicable University policies, procedures and regulations. All data, research records and materials and other intellectual property generated in University laboratories remain the property of the University.

Working Conditions:
This position works in a laboratory environment with potential exposure to biological and chemical hazards. The individual must be physically able to wear protective equipment and to provide standard care to research animals.
Salary Range:
Base pay is commensurate with experience.
The above statements are intended to describe the general nature and level of work performed by people assigned to this classification. They are not intended to be construed as an exhaustive list of all job duties performed by the personnel so classified. Management reserves the right to revise or amend duties at any time.
Required Qualifications
Education:
Ph.D., M.D. Or Equivalent Terminal Or Doctoral Degree.
Certifications/Professional Licenses:
No specific certification/professional license is required for this position.
Work Experience:
No specific work experience is required for this position.
Skills:
Not Applicable
Driver's License:
A driver's license is not required for this position.
More About This Job
At WashU, postdoctoral appointments:
  • Have a 5-year term limit that includes previous experience.

Preferred Qualifications
Education:
No additional education unless stated elsewhere in the job posting.
Certifications/Professional Licenses:
No additional certification/professional licenses unless stated elsewhere in the job posting.
Work Experience:
No additional work experience unless stated elsewhere in the job posting.
Skills:
Cell Cloning, Cell Culture Work, Cellular Biology Techniques, ChIP-Seq, Collaboration, CRISPR-Cas System, Data Analysis, Data Interpretations, Data Management, Experimentation, Laboratory Operations, Laboratory Techniques, Molecular Biology Techniques, PCR Methods, Researching, Results Reporting, RNA-Seq, Scientific Writing, Statistical Analysis, Unix Commands
Questions
For frequently asked questions about the application process, please refer to our External Applicant FAQ.
Accommodation
If you are unable to use our online application system and would like an accommodation, please email CandidateQuestions@wustl.edu or call the dedicated accommodation inquiry number at 314-935-1149 and leave a voicemail with the nature of your request.
All qualified individuals must be able to perform the essential functions of the position satisfactorily and, if requested, reasonable accommodations will be made to enable employees with disabilities to perform the essential functions of their job, absent undue hardship.
Pre-Employment Screening
All external candidates receiving an offer for employment will be required to submit to pre-employment screening for this position. The screenings will include criminal background check and, as applicable for the position, other background checks, drug screen, an employment and education or licensure/certification verification, physical examination, certain vaccinations and/or governmental registry checks. All offers are contingent upon successful completion of required screening.
Benefits Statement
Washington University in St. Louis is committed to providing a comprehensive and competitive benefits package to our employees. Benefits eligibility is subject to employment status, full-time equivalent (FTE) workload, and weekly standard hours. Please visit our website at https://hr.wustl.edu/benefits/ to view a summary of benefits.
EEO Statement
Washington University in St. Louis is committed to the principles and practices of equal employment opportunity. It is the University's policy to provide equal opportunity and access to persons in all job titles without regard to race, ethnicity, color, national origin, citizenship (where prohibited by federal law), age, religion, sex, sexual orientation, gender identity or expression, disability, protected veteran status, or genetic information.

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