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Causal Inference Machine Learning Postdoctoral Jobs in Beverly Hills, MO

TECHNOLOGY PARTNERS INC

Data Scientist

Clayton, MO · On-site

$67.50 - $96.43/hr

You will design experiments, apply causal inference techniques, and ensure clear documentation of ... Develop and implement deep learning models to forecast demand. * Extract, clean, and manipulate ...

Bunge North America

Data Scientist

Chesterfield, MO · On-site +1

Solid understanding of statistical inference, econometric modeling (e.g., time series analysis, causal inference), and machine learning algorithms (e.g., regression, classification, clustering, tree ...

Bunge

Data Scientist

Chesterfield, MO · On-site +1

Solid understanding of statistical inference, econometric modeling (e.g., time series analysis, causal inference), and machine learning algorithms (e.g., regression, classification, clustering, tree ...

Bunge

Data Scientist

Chesterfield, MO · On-site

... causal inference), and machine learning algorithms (e.g., regression, classification, clustering, tree-based models). • Demonstrated ability to frame complex problems, design analytical solutions ...

Bunge

Data Scientist

Chesterfield, MO · On-site

Solid understanding of statistical inference, econometric modeling (e.g., time series analysis, causal inference), and machine learning algorithms (e.g., regression, classification, clustering, tree ...

Bunge Iberica SA

Data Scientist

Chesterfield, MO · On-site

$110 - $150/hr

Solid understanding of statistical inference, econometric modeling (e.g., time series analysis, causal inference), and machine learning algorithms (e.g., regression, classification, clustering, tree ...

Enterprise Holdings

Data Scientist

Saint Louis, MO · On-site

$85 - $115/hr

Apply causal inference techniques using observational data to uncover relationships * Prepare and ... deep learning * Must have experience using libraries like tensorflow or pytorch * Must have ...

Enterprise Holdings

Data Scientist

Saint Louis, MO · Remote

Apply causal inference techniques using observational data to uncover relationships * Prepare and ... deep learning * Must have experience using libraries like tensorflow or pytorch * Must have ...

Enterprise Holdings

Data Scientist

Saint Louis, MO · On-site

Apply causal inference techniques using observational data to uncover relationships * Prepare and ... deep learning * Must have experience using libraries like tensorflow or pytorch * Must have ...

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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.

Senior Data Scientist

Tiger Analytics Inc.

Saint Louis, MO • On-site

Full-time

Re-posted 15 days ago

Job description

Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

As a Data Scientist you will be at the forefront of solving high-impact business problems using advanced machine learning, data engineering, and analytics solutions. The role demands a balanced mix of technical expertise, stakeholder management. You will design and analyze A/B tests and apply advanced techniques such as causal inference, matching models, and AutoML to generate reliable, actionable insights. Partnering closely with business and cross-functional teams, you will translate hypotheses into robust analytical models, validate outcomes with statistical rigor, and clearly communicate results to drive data-backed decision-making and measurable business value.

Key Responsibilities

  • Apply statistical techniques and machine learning methods to solve complex business problems.
  • Build, validate, and deploy predictive and analytical models using Python.
  • Perform data extraction, transformation, and analysis using SQL across large datasets.
  • Work on causal inference techniques such as causal ML, matching models, or uplift modeling to evaluate business interventions.
  • Collaborate with product, business, and engineering teams to translate requirements into scalable data solutions.
  • Present insights, findings, and recommendations clearly to technical and non-technical stakeholders.
  • Ensure data quality, model performance, and continuous improvement of analytical workflows.

Requirements

  • 8 years of experience in data science and ML models
  • Hands-on expertise in Python and SQL for data analysis and modelling.
  • In-depth experience with A/B testing or similar methodologies such as Causal ML, Matching Models, or Auto ML.
  • Strong problem-solving skills with the ability to work independently on end-to-end data science projects.
  • Excellent communication skills and stakeholder management experience.
  • Solid understanding of regression, classification, and statistical methods

Benefits

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.