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Causal Inference Machine Learning Postdoctoral Jobs in San Jose, CA

Apple Inc.

Senior Data Scientist, Apple Ads

Cupertino, CA · On-site

$147.40 - $272.10/hr

Strong foundation in statistics, experimentation, causal inference, and analytical problem solving * Experience developing statistical, machine learning, econometric, forecasting, or optimization ...

Socket.dev

Senior Data Scientist - Applied AI

Palo Alto, CA · On-site

$149 - $286/hr

Apply statistical inference, causal analysis, and machine learning techniques to solve challenging product and operational problems * Develop scalable evaluation methodologies for Responsible AI ...

New

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 ...

Walmart

Staff, Data Scientist (Pricing)

Sunnyvale, CA · On-site +1

Our work combines techniques from forecasting, optimization, operations research, machine learning (classical ML, deep learning, reinforcement learning), causal inference, experimentation, and ...

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Causal Inference Machine Learning Postdoctoral information

See San Jose, CA salary details

$41.6K

$63.5K

$71.5K

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

As of Aug 11, 2026, the average yearly pay for causal inference machine learning postdoctoral in San Jose, CA is $63,549.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,700.00 and $66,200.00 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 are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in San Jose, CA? For Causal Inference Machine Learning Postdoctoral jobs in San Jose, CA, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in San Jose, CA look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in San Jose, CA are:
What cities near San Jose, CA are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities near San Jose, CA with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in San Jose, CA as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 21% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $63,549 per year, or $30.6 per hour.

Applied Science / Data Science Leader

Attentive

San Francisco, CA

$320K - $380K/yr

Full-time

Re-posted 3 days ago

Job description

About the Role

Our Applied Science / Data Science team is a world-class organization focused on using data, experimentation, machine learning, and AI to shape product strategy and accelerate business growth. We partner closely with Product, Engineering, Marketing, Sales, and Customer Success to build intelligent products, optimize customer experiences, and drive measurable business impact.

This will be a leadership role overseeing our applied science / data science team and you will lead a team of high-performing data scientists responsible for solving some of the company's most strategic product and business challenges. You will define the vision for applied data science across multiple product areas, develop innovative analytical and machine learning solutions, and partner with senior leaders to influence company strategy. This is a highly visible leadership role that blends people management, technical excellence, and cross-functional influence.

What You'll Accomplish

  • Lead, mentor, and grow a team of Applied Scientists / Data Scientists, fostering technical excellence and career development
  • Define and execute the applied data science roadmap in partnership with Product, Engineering, and executive stakeholders
  • Drive the development of statistical models, machine learning solutions, experimentation frameworks, and causal inference methodologies to improve product performance and customer outcomes
  • Establish best practices for experimentation, measurement, forecasting, and decision-making across the organization
  • Translate ambiguous business problems into scalable analytical and machine learning solutions
  • Influence product strategy by identifying opportunities through deep analysis of customer behavior, experimentation, and business performance
  • Partner closely with engineering teams to operationalize models and deploy production-ready solutions
  • Present insights and recommendations to senior leadership and executive stakeholders to influence strategic decisions
  • Build a culture of scientific rigor, operational excellence, and continuous innovation across the data science organization

Your Expertise

  • Master's or Ph.D. in Statistics, Computer Science, Economics, Operations Research, Mathematics, Machine Learning, or a related quantitative field; Bachelor's degree with equivalent industry experience also considered
  • 8+ years of experience in Data Science, Machine Learning, Analytics, or related disciplines
  • 3+ years of experience managing and developing high-performing data science teams
  • Deep expertise in experimentation, causal inference, statistical modeling, predictive modeling, and machine learning
  • Experience partnering closely with Product and Engineering organizations to deliver data-driven product improvements
  • Strong proficiency in Python and SQL with experience working on large-scale datasets
  • Excellent communication skills with the ability to influence executive stakeholders through data-driven storytelling
  • Demonstrated success leading cross-functional initiatives from ideation through production

You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work.

For US based applicants:

  • The US base salary range for this full-time position is $320,000 - $380,000 annually + equity + benefits
  • Our salary ranges are determined by role, level and location

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