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Causal Inference Machine Learning Postdoctoral Jobs in Daly City, CA

Tonal

Senior Data Scientist

San Francisco, CA · On-site +1

$165K - $190K/yr

Overview This Senior Data Scientist will drive causal and machine learning-based analyses to ... Strong statistical experience in causal inference methods like Difference in Difference, propensity ...

SwiftCruit

Senior Data Scientist

San Francisco, CA · On-site

$170 - $200/hr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Strong foundation in statistics, machine learning, experimentation, and causal inference, with experience applying these methods to solve real-world business problems. * Strong coding and debugging ...

OpenAI

Economist

San Francisco, CA · On-site

$266K - $385K/yr

Familiarity with causal inference, machine learning methods, or structural modeling. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial ...

MasterCard

Senior Data Scientist

San Francisco, CA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Design, build, and deploy machine learning models for ad targeting, ranking, and bidding ... Strong background in incrementality measurement, experimentation, A/B testing, causal inference ...

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Showing results 21-40

All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Daly City, CA

Causal Inference Machine Learning Postdoctoral information

See Daly City, CA salary details

$41.1K

$62.8K

$70.7K

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

As of Aug 15, 2026, the average yearly pay for causal inference machine learning postdoctoral in Daly City, CA is $62,828.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,000.00 and $65,500.00 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?

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 Daly City, CA are hiring for Causal Inference Machine Learning Postdoctoral jobs?

Cities near Daly City, CA with the most Causal Inference Machine Learning Postdoctoral job openings:

Causal Inference Machine Learning Postdoctoral jobs near you

Senior Data Scientist - Experimentation & Measurement

PlayStation Global

San Mateo, CA

Full-time

Re-posted 8 days ago

Job description

Senior Data Scientist -  Experimentation & Measurement

San Mateo, CA

 Overview:

As a Senior Data Scientist on the Decision Science team within the Data Science, Analytics, & Enablement (DSAE) organization at PlayStation, you will take a leading role in designing and interpreting experiments that evaluate the impact of PS4 to PS5 user migration initiatives, growth marketing strategies, and broader campaign performance. This role is focused on advancing our experimentation practices-bringing statistical rigor, clear measurement strategies, and deep causal inference expertise to some of the most critical initiatives across PlayStation.

What You'll Be Doing:
  • Lead the design, execution, and interpretation of A/B tests and quasi-experiments to evaluate the impact of user migration initiatives (PS4 to PS5), growth marketing strategies, and campaign performance.
     
  • Partner with cross-functional teams (product, engineering, marketing) to embed experimentation into development and iteration cycles.
     
  • Serve as a thought leader on best practices for hypothesis development, metric selection, test structure, and results communication.
     
  • Apply advanced causal inference methods when experimentation isn't feasible or to inform test design and prioritization.
     
  • Help define and contribute to centralized experimentation frameworks, tools, and documentation to scale best practices across the company.
     
  • Independently extract, transform, and analyze data from complex systems using SQL, Python, and other analytics tools.
     
  • Communicate findings clearly to technical and non-technical stakeholders, helping drive business decisions with rigor and clarity.
     
  • Stay current on new methodologies in experimentation and causal analysis, and bring fresh perspectives to the team's work.
Basic Requirements:
 

  • Bachelor's degree or equivalent.
     

  • 5+ years of experience in a data science experimentation-focused role (3+ with PhD).
     

  • Deep expertise in A/B testing and causal inference, including quasi-experimental methods.
     

  • Proficiency in SQL for data extraction and transformation.
     

  • Proficiency in Python, including statistical and data science libraries.
     

  • Broad and applied knowledge of statistical techniques and machine learning modeling methods.
     

  • Proven ability to influence product and business decisions through clear, actionable insights.
     

  • Experience contributing to or developing experimentation frameworks, best practices, or internal tooling.

    Preferred Requirements:

  • Master's or PhD in Statistics, Economics, or Econometrics. Other degrees in quantitative disciplines may be considered.
     

  • Bonus: Interest in or knowledge of video games, gaming platforms, or player behavior.

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