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Entry Level Meta Data Scientist information

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How much do entry level meta data scientist jobs pay per year?

As of Jul 13, 2026, the average yearly pay for entry level meta data scientist in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What is the difference between Entry Level Meta Data Scientist vs Entry Level Data Analyst?

AspectEntry Level Meta Data ScientistEntry Level Data Analyst
Required CredentialsBachelor's in Computer Science, Data Science, or related field; familiarity with machine learning and programmingBachelor's in Statistics, Mathematics, or related field; proficiency in data visualization and basic analytics
Work EnvironmentTech companies, e-commerce, social media platforms, often collaborative and project-basedBusiness, finance, healthcare sectors; focus on reporting and data interpretation
Employer & Industry UsageUsed in tech-driven industries focusing on data modeling and machine learningCommon across various industries for data reporting and insights

Entry Level Meta Data Scientists focus on developing machine learning models and working with large datasets, often in tech environments. Entry Level Data Analysts primarily interpret data, create reports, and support decision-making. Both roles require strong analytical skills but differ in technical complexity and focus areas.

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What cities are hiring for Entry Level Meta Data Scientist jobs? Cities with the most Entry Level Meta Data Scientist job openings:
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Entry Level Meta Data Scientist jobs near you
Infographic showing various Entry Level Meta Data Scientist job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.
Data Scientist II, Experimentation

Data Scientist II, Experimentation

Pinterest

San Francisco, CA • On-site, Remote

Other

Re-posted 25 days ago

Job description

We are seeking a data scientist with a strong background in experimentation and statistical analysis to help us improve and iterate on our experimentation platform. The successful candidate will play a key role in improving our experiment processes at scale, leveraging their expertise to drive innovation and help make sure that Pinterest users are receiving the most thoroughly data-driven features. With thousands of experiments running concurrently, the magnitude of our operations presents a significant opportunity for impact. If you possess a strategic mindset, proven experience in experimental design and analysis, and a passion for driving results, we invite you to join us in shaping the future of experimentation.

What you'll do:;

  • Comb through the literature in experimentation to identify potential methodologies that can improve parts of our platform where we have the biggest opportunities.
  • Make the process of setting up, running and evaluating experiments smoother and more repeatable for our platform users, ensuring that decisions are risk-aware and consistent
  • Write workflows to make our vast experimentation meta-data able to be leveraged by our team and outside of our team to better understand the experimentation landscape.
  • Consult with product data science teams to debug, design or improve their experiments and experimentation process.

What we're looking for:

  • PhD in a relevant field (stats, applied math, biostatistics, etc...) OR 2+ years of hands-on experience working as a data scientist or applied scientist.
  • Experience working directly on experimentation problems and an awareness of state of the art methodologies.
  • The ability to write clean, efficient, and scalable code that can be easily maintained and extended by other team members.
  • Proficiency in software development best practices, including version control systems such as Git, to ensure efficient collaboration, code management, and reproducibility in a data science environment.
  • Familiarity with workflow management tools such as Apache Airflow to create and schedule data pipelines, allowing for automated and reliable execution of machine learning workflows.

In-Office Requirement Statement:

  • We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
  • This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.

Relocation Statement:

  • This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

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