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Causal Inference Machine Learning Postdoctoral Jobs in San Mateo, CA

Slickdeals

Sr. Data Scientist II

San Mateo, CA · On-site

$175K - $205K/yr

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 ...

Slickdeals

Sr. Data Scientist II

San Mateo, CA · On-site

$175K - $205K/yr

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 ...

Roku

Senior Research Data Scientist

San Jose, CA · On-site

$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 ...

Neura Market

Economist

San Francisco, CA · On-site

$180 - $240/hr

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 ...

NerdWallet, Inc

Staff Data Scientist

San Francisco, CA · On-site

$130 - $180/hr

Shape long‑term strategy for personalization, experimentation, and AI‑driven growth at NerdWallet Your experience: * 8+ years of experience in applied machine learning, causal inference ...

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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 23, 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 II

Socket.dev

Mountain View, CA • On-site

$150 - $210/hr

Other

Posted 6 days ago

Job description

About Gen:

Gen is a global company dedicated to powering Digital Freedom through its trusted consumer brands including Norton, Avast, LifeLock, MoneyLion and more. Our combined heritage is rooted in financial empowerment and cyber safety for the first digital generations, and today we deliver award-winning cybersecurity, online privacy, identity protection and financial wellness solutions to nearly 500 million users in more than 150 countries.

Together, we share a collective passion and vision to protect consumers and help them grow, manage and secure their digital and financial lives. We’re always looking for smart, fearless and high-impact talent who see AI as a teammate – leveraging it to move faster and deliver meaningful results.

When you’re part of Gen, you’ll have the flexibility, tools and support to do your best work and grow your career – from flexible working options and time off to competitive pay, benefits and well-being programs.

At Gen, we are scrappy and relentlessly customer driven. We create room for healthy debate, experimentation and continuous learning, and we seek out people with different experiences, identities and ideas to join our team. You’ll work with people who back each other, respect each other and understand that our differences are a competitive advantage.

If this sounds like you, we’d love you to be part of Gen.

About The Role:

Our team is a core part of Gen’s AI transformation. We build machine learning systems that directly improve customer growth, retention, personalization, pricing, recommendations, billing success, and long-term customer value across a large global consumer portfolio.

This role focuses on applied machine learning, experimentation, and business-impact modeling. You will build practical models that personalize customer decisions across in-app messages, email, portals, billing flows, and lifecycle journeys.

We are looking for a hands-on AI / Machine Learning Engineer who can frame business problems, build models, design experiments, measure impact rigorously, and partner with engineering and product teams to bring models into production. Experience with recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization is a strong plus.

Key Responsibilities:
  • End-to-end ML ownership: Independently lead applied machine learning initiatives from data preparation and model development through experimentation, production deployment, monitoring, and continuous optimization.
  • Productionization and MLOps: Deploy and operate scalable ML solutions with robust workflows for batch or real-time inference, evaluation, monitoring, observability, versioning, retraining, rollback, and continuous model iteration.
  • Experimentation and impact measurement: Design and analyze A/B tests, holdouts, and validation frameworks to measure incremental customer and business outcomes.
  • Advanced model development: Design and build propensity, response, uplift, recommendation and ranking, contextual bandit, segmentation, optimization, and customer-value models.
  • Cross-functional delivery: Partner with ML infrastructure, data engineering, backend engineering, product, analytics, and business teams to integrate models into reliable production systems.
  • AI-first engineering workflows: Build agentic tools, automation, and reusable modules that streamline model development and MLOps workflows, improve productivity, and increase the speed, quality, and consistency of ML delivery.
About You:Education:

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.

A Master’s or PhD in a quantitative field is a plus, but not required.

Experience:
  • Applied ML experience: Five or more years of professional experience in applied machine learning, data science, ML engineering, applied statistics, or a related field, or equivalent demonstrated impact.
  • Large-scale data: Experience building and evaluating models using large-scale behavioral, transactional, product, marketing, or customer data.
  • Experimentation: Experience designing experiments, defining success metrics, measuring incrementality, interpreting results, and translating findings into practical product or business decisions.
  • Production collaboration and ML operations: Experience partnering with engineering, product, analytics, and business teams to deploy and operate production ML systems, including inference pipelines, monitoring, observability, retraining, and cloud-based MLOps workflows.
  • Relevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, optimization, or lifecycle decisioning is a strong plus.
Skills:
  • Machine learning and modeling: Strong Python skills and hands-on experience with common ML frameworks, supervised learning, model selection, hyperparameter tuning, evaluation, and performance diagnosis.
  • Data processing and feature engineering: Strong SQL skills and experience with BigQuery, Spark, or similar platforms for data collection, cleaning, preprocessing, exploration, and feature development.
  • Analytics and experimentation: Strong statistical reasoning and practical knowledge of A/B testing, holdout design, causal measurement, incrementality, statistical significance, and business-impact analysis.
  • Production engineering and MLOps: Experience with cloud ML platforms, deployment pipelines, batch or real-time inference, CI/CD, model registries, monitoring, observability, retraining, rollback, and scalable system design.
Personal Attributes:
  • Strong ownership: Takes responsibility for delivering high-quality solutions and measurable outcomes with limited oversight.
  • Business-impact orientation: Connects modeling and engineering decisions to customer experience, product performance, and business value.
  • AI-first builder mindset: Enjoys coding, modeling, automating, and shipping while proactively using AI and agentic tools to improve productivity and quality.
  • Clear, collaborative communication: Communicates assumptions, tradeoffs, risks, and results effectively across ML, engineering, product, analytics, and business teams.
What’s Next:

Our hiring process includes the following steps:

  1. Video Introduction: Submit a brief video introducing yourself, your work, and your most relevant experience.
  2. Technical interview: Demonstrate your applied machine learning, analytical, and engineering capabilities.
  3. Hiring manager interview: Meet with the hiring manager to discuss your background and fit for the role.
  4. Final interview: Meet with our AI leadership, including the Chief AI Officer, for a final assessment.
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