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Causal Inference Machine Learning Postdoctoral Jobs in San Jose, 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 ...

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Causal Inference Machine Learning Postdoctoral information

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$41.6K

$63.5K

$71.5K

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

As of Aug 6, 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.

Senior Applied Scientist, Causal Inference

LinkedIn

Mountain View, CA • On-site, Remote

$139K - $229K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

LinkedIn rating

9.3

Company rating: 9.3 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

15th of 242 rated software companies

Job description

Company Description

LinkedIn is the worlds largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. Were also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture thats built on trust, care, inclusion, and fun where everyone can succeed. Join us to transform the way the world works.

Job Description

LinkedIn's Data Science team leverages big data to empower business decisions and deliver data-driven insights, metrics, and tools in order to drive member engagement, business growth, and monetization efforts. With over 1 billion members around the world, a focus on great user experience, and a mix of B2B and B2C programs, a career at LinkedIn offers countless ways for an ambitious data scientist to have an impact.

We are now looking for a talented and driven individual to accelerate our efforts and be a major part of our data-centric culture. It is expected that this person understands experimentation and/or machine learning techniques to be able to implement from scratch and have the ability to extend and enhance these techniques to specific applications like business problems. Successful candidates will exhibit technical acumen on inference and algorithms, and the business savviness to use these technical skills to drive better business decision-making.

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.

Responsibilities

  • Work with a team of high-performing analytics, data science professionals, and cross-functional teams to identify business opportunities and develop algorithms and methodologies to address them.
  • Analyze large-scale structured and unstructured data.
  • Conduct in-depth and rigorous data science research, model improvement, advanced experiments, observational causal studies to quantify the cause and effect in the ecosystem, identify business opportunities and to drive member value and customer success.
  • Develop methodologies to enhance LinkedIn's product and platform capabilities.
  • Engage with technology partners to build, prototype and validate scalable tools/applications end to end (backend, frontend, data) for converting data to insights
  • Promote and enable adoption of technical advances in Data Science; elevate the art of Data Science practice at LinkedIn.
  • Improve LinkedIn's ability to measure and credibly speak to labor market trends and other economic phenomena.
  • Initiate and drive projects to completion independently
  • Act as a thought partner to senior leaders to prioritize/scope projects, provide recommendations and evangelize data-driven business decisions in support of strategic goals
  • Partner with cross-functional teams to initiate, lead or contribute to large-scale/complex strategic projects for team, department, and company
  • Provide technical guidance and mentorship to junior team members on solution design as well as lead code/design reviews
Qualifications

Basic Qualifications

  • Bachelor's Degree in a quantitative discipline: Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics, Economics, etc.
  • 3+ years of industry or relevant academia experience
  • Background in at least one programming language (eg. R, Python, Java, Ruby, Scala/Spark or Perl)
  • Experience in applied statistics and statistical modeling in at least one statistical software package, (eg. R, Python)

Preferred Qualifications

  • BS and 5+ years of relevant work experience, MS and 3+ years of relevant work experience, or Ph.D. and 1+ years of relevant work/academia experience
  • MS or PhD in a quantitative discipline: Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics, Economics, etc.

Suggested Skills

  • Machine Learning
  • Research
  • Causal Inference

You will Benefit from our Culture

We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels.

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $139,000.00 to $229,000.00 Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.

The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits. 

Additional Information

Equal Opportunity Statement 

We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful.

If you need a Reasonable Accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us and describe the specific Accommodation requested for a disability-related limitation.
Fill out an Accommodation request here: https://app.smartsheet.com/b/form/b660a0327d044969abfd7a4e73d15c36

Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to:

  • Documents in alternate formats or read aloud to you
  • Having interviews in an accessible location
  • Being accompanied by a service dog
  • Having a sign language interpreter present for the interview

A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response.

LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information.

San Francisco Fair Chance Ordinance

Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.

Pay Transparency Policy Statement

As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.

Global Data Privacy Notice and Compliance Posters for Job Candidates 

Please use this link to access documents that provide information about how LinkedIn handles the personal data of employees and job applicants, as well as the E-Verify Participation Notice and the Department of Justice Immigrant and Employee Rights Section Right to Work posters: https://www.linkedin.com/legal/candidate-portal.

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