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Causal Inference Machine Learning Postdoctoral information

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$30.7K

$46.8K

$52.7K

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

As of Jul 15, 2026, the average yearly pay for causal inference machine learning postdoctoral in Humble, TX is $46,822.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,200.00 and $48,800.00 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.

What cities near Humble, TX are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities near Humble, TX with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Machine Learning Engineer - Platforms

Machine Learning Engineer - Platforms

MD Anderson

Houston, TX • On-site

$123K/yr

Full-time

Medical, Dental, Retirement, PTO

Posted yesterday

New

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 169 frontline employees who took The Breakroom Quiz

27th of 885 rated healthcare providers

Job description

As a Machine Learning Engineer - Platforms within the Data Impact & Governance organization, you will shape and scale the enterprise AI/ML platform that powers clinical, research, and operational machine learning across the institution. This is a hands-on engineering role with direct influence on how data science workflows operate institution-wide-enabling safe, efficient, and high-impact AI delivery.
You'll work with modern cloud and container technologies, MLOps frameworks, and enterprise-grade tools while building solutions that improve patient care, strengthen operations, and accelerate scientific discovery.
What's in it for you?
  • Exceptional Benefits: Enjoy paid medical benefits, generous paid time off (PTO), strong retirement plans, and a comprehensive benefits package designed to support your total well-being.
  • High-Impact Work: Develop and maintain the platforms that allow clinicians, researchers, and data scientists to bring AI solutions into real-world healthcare environments.
  • Cutting-Edge Technology: Work with Dataiku, Kubernetes, Azure, container technologies, and MLOps frameworks that support large-scale enterprise ML operations.
  • Career Growth: Collaborate with ML engineers, data scientists, architects, and IT teams, gaining exposure to complex, enterprise-wide AI initiatives and governance.
  • Mission-Driven Culture: Your work will contribute directly to improving patient outcomes and advancing research at a nationally recognized cancer center.

Summary
The Machine Learning Engineer - Platforms supports the development, reliability, and scalability of the enterprise AI/ML platform used across clinical and business operations. The role focuses on MLOps engineering, platform integration, automation, container management, model monitoring, and lifecycle governance. The engineer partners closely with data scientists, ML engineers, and enterprise IT teams to support AI development and deployment, while ensuring compliance, performance, and responsible AI practices.
Major Work Activities
Technical Expertise
  • Support development, administration, and maintenance of the enterprise AI/ML platform (Dataiku, Kubernetes, Azure), ensuring scalability, reliability, and smooth integration with institutional systems.
  • Orchestrate training, deployment, and inference pipelines within Dataiku targeting Azure and on-premises Kubernetes clusters.
  • Develop and maintain MLOps workflows for reproducibility, version control, governance, and model lifecycle management.
  • Manage and optimize containerized environments using Docker and Kubernetes to support data science workloads.
  • Provide platform support for data scientists and ML engineers, troubleshooting environment, pipeline, and dependency issues.
  • Monitor platform performance, cost, security, and compliance, ensuring alignment with enterprise and regulatory standards.

Analytical Skills
  • Build and support scalable pipelines in Dataiku, Kubernetes, and Azure, including feature engineering, model tracking, and validation workflows.
  • Debug, test, and resolve complex platform or pipeline issues using strong analytical and problem-solving skills.
  • Assist with healthcare data integration using standards such as HL7, FHIR, or DICOM when required for model development.

Professionalism: Oral & Written Communication
  • Share platform knowledge, best practices, and methodologies through training, documentation, and cross-team collaboration.
  • Support analytics and automation workflows by enabling access to data, reviewing project requests, and assisting with interpretation.
  • Communicate platform updates, risks, performance, and issue resolutions clearly during meetings and collaborative sessions.
  • Work effectively with leaders, technical peers, and end users, ensuring strong communication across both technical and non-technical stakeholders.

Other Duties
  • Perform additional tasks as assigned to support the AI/ML platform, MLOps practices, and enterprise data science initiatives.

EDUCATION
  • Required: Bachelor's Degree Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline.
  • Preferred: Master's Degree Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline.

WORK EXPERIENCE
  • Required: 3 years in machine learning engineering, data science, data engineering, and/or software engineering experience.
  • Required: 1 year experience with Master's degree.
  • No experience required with PhD.

Preferred Experience/Skills: Healthcare experience needed, experience with MLOps platforms and/or cloud AI certifications, strong proficiency in CI/CD and automation of the AI lifecycle, experience working on healthcare focused machine learning projects. Experience with Azure and/or Kubernetes. Proficiency in services such as Azure Kubernetes Services and Azure ML (or similar).
The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state, or local laws unless such distinction is required by law.http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html
Additional Information
  • Requisition ID: 178799
  • Employment Status: Full-Time
  • Employee Status: Regular
  • Work Week: Days
  • Minimum Salary: US Dollar (USD) 123,000
  • Midpoint Salary: US Dollar (USD) 154,000
  • Maximum Salary : US Dollar (USD) 185,000
  • FLSA: exempt and not eligible for overtime pay
  • Fund Type: Hard
  • Work Location: Remote (within Texas only)
  • Pivotal Position: Yes
  • Referral Bonus Available?: Yes
  • Relocation Assistance Available?: Yes

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