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Summer Causal Inference Jobs in California (NOW HIRING)

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Summer Causal Inference information

What is a summer causal inference?

A Summer Causal Inference position is typically a short-term research or internship role focused on applying statistical methods to determine causal relationships between variables, often in fields like economics, public policy, or data science. Individuals in this position work on projects that require designing experiments or analyzing observational data to infer causality rather than just correlation. The role is ideal for students or early-career professionals seeking hands-on experience in causal inference techniques during the summer months.

What types of projects and methodologies can I expect to work on as a summer causal inference intern?

As a Summer Causal Inference intern, you'll typically work on projects involving the design and analysis of experiments or observational studies to determine cause-and-effect relationships. You may use methodologies such as propensity score matching, difference-in-differences, instrumental variables, or regression discontinuity designs. Collaboration with data scientists, economists, and business stakeholders is common, as you'll help translate findings into actionable insights. Expect to handle real-world datasets and communicate your results through presentations or reports, gaining valuable experience in both technical and applied aspects of causal inference.

What are the key skills and qualifications needed to thrive as a summer causal inference researcher, and why are they important?

To thrive as a Summer Causal Inference Researcher, you need a solid background in statistics, econometrics, and data analysis, typically supported by coursework or a degree in a quantitative field. Familiarity with statistical programming languages like R or Python and experience using tools such as STATA or MATLAB are often required. Strong problem-solving skills, attention to detail, and the ability to communicate complex concepts clearly are valuable soft skills. These skills and qualities are crucial for accurately identifying causal relationships in data and effectively collaborating within interdisciplinary research teams.

What are the most commonly searched types of Causal Inference jobs in California?

The most popular types of Causal Inference jobs in California are:

What are popular job titles related to Summer Causal Inference jobs in California?

For Summer Causal Inference jobs in California, the most frequently searched job titles are:

What job categories do people searching Summer Causal Inference jobs in California look for?

The top searched job categories for Summer Causal Inference jobs in California are:

What cities in California are hiring for Summer Causal Inference jobs?

Cities in California with the most Summer Causal Inference job openings:

Summer Causal Inference jobs near you

Business and Marketing Data Scientist

Socket.dev

Mountain View, CA • On-site

$177.55 - $198/hr

Other

Posted 12 days ago

Job description

MINIMUM QUALIFICATIONS:

  • PhD degree in Economics, Statistics, Biostatistics or a related field, and experience in the job offered or in a Business and Marketing Data Scientist-related occupation.

  • Position requires experience in the following: Causal inference; Bayesian statistics; Machine Learning; R or Python; and SQL.


ABOUT THE JOB:

At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.


As a Business Data Scientist on the YouTube Business Go-To-Market Impact Measurement team, you will work closely with business leaders to help shape the future of YouTube. You will leverage rigorous techniques from causal inference, advanced statistical modeling, and machine learning. It will be your responsibility to determine the best approach for solving problems and to communicate clearly with decision-makers who may or may not have a strong technical background.


We are looking for a detail-oriented problem solver with broad knowledge of causal inference, Bayesian statistics, and machine learning. We use a large set of methodologies, ranging from experimental to observational techniques, touching on a broad range of problems from different functional and product areas. The ideal candidate is comfortable wearing multiple hats and is passionate about conducting causal studies and helping stakeholders implement data-driven decisions. Creative problem-solving and stakeholder management skills are critical.


Given the nature of the position, someone who loves to learn new things will be successful. Effective data scientists on our team keep up with advances in the causal inference literature. When existing methodologies are not well suited for the problem at hand, you will be encouraged to develop new methods in collaboration with your teammates and work with our summer interns on research projects. We also attend relevant conferences and organize internal events to keep our toolkit updated and to educate the broader Google community.


The US base salary range for this full-time position is $177,550 - $198,000 + 15% bonus target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google https://www.google.com/about/careers/applications/benefits/.


Position reports to the Google Mountain View, CA office & may allow for a hybrid schedule as per Google policy.


RESPONSIBILITIES:

  • Design and execute causal studies to address critical business questions.

  • Leverage advanced statistical models to find business insights in experimental and observational data.

  • Present and communicate actionable insights and recommendations to executives and cross-functional partners.

  • Serve as a peer reviewer and consultant for causal studies across the organization.

  • Stay current with the latest advancements in causal inference.

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