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Causal Inference Machine Learning Postdoctoral Jobs in San Mateo, CA

CYNET SYSTEMS

Principal Data Scientist

Oakland, CA · On-site

$128 - $148/hr

... Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field. * Expertise in experimental design and causal inference methods.

Roku

Senior Research Data Scientist

San Jose, CA

$330K - $375K/yr

PhD in Economics, Econometrics, Statistics, or a closely related quantitative field with a strong emphasis on causal inference * 10+ years of experience applying causal inference and machine learning ...

Intuit

Senior Data Scientist

Mountain View, CA

$149K - $202K/yr

This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...

Intuit

Senior Data Scientist

Mountain View, CA · On-site

$149K - $202K/yr

This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...

LinkedIn

Senior Applied Scientist

Mountain View, CA · On-site +1

$144K - $236K/yr

LinkedIn's Data Science and Applied Science teams use data, experimentation, causal inference, machine learning, and AI to solve important product and business problems. With more than 1 billion ...

Socket.dev

Sr. Data Scientist

San Mateo, CA · On-site

$145 - $175/hr

Design, execute, and interpret A/B tests to evaluate product changes and validate hypotheses using causal inference, machine learning methodologies, and experimentation best practices. * Develop ...

LinkedIn

Senior Applied Scientist

Mountain View, CA · On-site

$144K - $236K/yr

LinkedIn's Data Science and Applied Science teams use data, experimentation, causal inference, machine learning, and AI to solve important product and business problems. With more than 1 billion ...

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in San Mateo, CA

Causal Inference Machine Learning Postdoctoral information

See San Mateo, CA salary details

$40.4K

$61.8K

$69.5K

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

As of Aug 22, 2026, the average yearly pay for causal inference machine learning postdoctoral in San Mateo, CA is $61,760.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,900.00 and $64,400.00 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.

What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in San Mateo, CA look for?

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in San Mateo, CA are:

What cities near San Mateo, CA are hiring for Causal Inference Machine Learning Postdoctoral jobs?

Cities near San Mateo, CA with the most Causal Inference Machine Learning Postdoctoral job openings:

Causal Inference Machine Learning Postdoctoral jobs near you

AI & Machine Learning Engineer I

Jobtailor

Mountain View, CA • On-site

$140 - $210/hr

Other

Posted 5 days ago

Job description

  • Own well-defined machine learning projects from data exploration and model development through validation, deployment, and iteration.
  • Build and improve predictive, recommendation, ranking, segmentation, uplift, and customer-value models for customer personalization and decisioning.
  • Prepare datasets, define modeling targets, develop features, and ensure data quality for training and evaluation.
  • Design and analyze A/B tests, holdouts, and offline evaluations to measure model performance and business impact.
  • Work with engineering, product, analytics, and business partners to integrate models into production and improve them based on results and feedback.
  • Use AI coding assistants, automation, and reusable tools to improve the speed, quality, and consistency of modeling and analytical workflows.
Requirements
  • Degree requirements are flexible. A technical degree in Computer Science, Data Science, Statistics, Mathematics, Operations Research, Economics, Engineering, or a related field is helpful, but equivalent practical experience is equally valued.
  • Applied ML and model development: Two or more years of professional experience in applied machine learning, data science, ML engineering, applied statistics, or a related field, including experience building and evaluating models with real-world data.
  • Data analytics: Experience analyzing behavioral, transactional, product, marketing, or customer data and translating findings into practical insights or recommendations.
  • Experimentation: Experience defining success metrics, analyzing experiments, evaluating model performance, and interpreting business impact.
  • Collaborative delivery: Experience working with engineering, product, analytics, or business partners to deploy or apply data-driven solutions.
  • Relevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, or lifecycle decisioning is a plus.
  • Machine learning and modeling: Strong Python skills and practical knowledge of supervised learning, model selection, hyperparameter tuning, evaluation, and performance analysis.
  • Data processing and feature engineering: Strong SQL skills and experience using platforms such as BigQuery, Spark, or similar tools for data extraction, cleaning, preprocessing, exploration, and feature development.
  • Analytics and experimentation: Strong analytical and statistical reasoning, including A/B testing, holdout design, statistical significance, incrementally, and business-impact measurement.
  • Technical tools and workflows: Familiarity with common ML libraries, cloud data or ML platforms, version control, and AI-assisted development tools.
  • Ownership mindset: Takes responsibility for assigned work, follows through on commitments, and proactively addresses issues.
  • Business-impact orientation: Connects modeling and analysis to customer experience and measurable outcomes.
  • AI-first builder mindset: Enjoys modeling, analyzing, automating, and shipping while using AI tools to improve productivity and quality.
  • Growth mindset: Learns quickly, seeks feedback, and continuously develops technical and business knowledge.
  • Clear, collaborative communication: Communicates ideas, assumptions, results, and challenges effectively with technical and non-technical partners.
Core Competencies

Demonstrates expertise in applied machine learning, data analytics, and model development, with strong skills in Python and SQL for data processing and feature engineering. Proven ability to collaborate with cross-functional teams to deploy data-driven solutions and measure business impact through experimentation and analytics.

Highest-signal resume keywords
  • Applied Machine Learning
  • Data Analytics
  • Model Development
  • Python Programming
  • SQL Proficiency
ATS Optimization Keywords Hard Skills
  • Machine Learning
  • Model Evaluation
  • Feature Engineering
  • A/B Testing
  • Statistical Analysis
  • Hyperparameter Tuning
  • Data Processing
  • Predictive Modeling
  • Recommendation Systems
  • Causal Inference
Soft Skills
  • Collaborative Communication
  • Ownership Mindset
  • Growth Mindset
Industry Keywords
  • Customer Personalization
  • Decisioning
  • Business Impact Measurement
  • Behavioral Data Analysis
  • Transactional Data Analysis
Tools & Technologies
  • BigQuery
  • Spark
  • ML Libraries
  • Cloud Data Platforms
  • AI Coding Assistants
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