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Statistics Validator Jobs in Bothell, WA (NOW HIRING)

Biostatistician

Renton, WA · Remote

$60 - $65/hr

Author and review evaluation tasks that require deriving, reproducing, or validating statistical outputs from clinical datasets and associated tables, figures, and listings (TFLs). * Apply expert ...

Biostatistician

Everett, WA · Remote

$60 - $65/hr

Author and review evaluation tasks that require deriving, reproducing, or validating statistical outputs from clinical datasets and associated tables, figures, and listings (TFLs). * Apply expert ...

Key job responsibilities - Design, develop, validate, and maintain machine learning models, statistical analyses, and decision frameworks supporting one or more of the following pillars: Fire TV ...

Senior Applied Scientist

Redmond, WA · On-site

$160K - $261K/yr

You will evaluate existing algorithms, identify opportunities for improvement, and validate your ... Mathematical and Statistical modeling, Machine and Reinforcement Learning, Optimization Methods ...

Required : • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or ... training, validation, deployment). • Experience with offline evaluation and online A/B ...

Showing results 41-60

Statistics Validator information

See Bothell, WA salary details

$45.3K

$93.5K

$130.8K

How much do statistics validator jobs pay per year?

As of Sep 9, 2026, the average yearly pay for statistics validator in Bothell, WA is $93,519.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,700.00 and $129,700.00 per year, depending on experience, location, and employer.

What is a statistics validator?

Statistics Validators are professionals who verify the accuracy, integrity, and reliability of statistical data and analyses. They review datasets, methodologies, and statistical outputs to ensure that findings are valid and meet relevant standards. Their work is crucial in research, government, and industry settings, where credible data is essential for decision making. By checking for errors, inconsistencies, and biases, Statistics Validators help maintain the quality and trustworthiness of statistical information.

What are the primary challenges a statistics validator faces when ensuring data integrity within a project?

Statistics Validators often encounter challenges related to data quality, such as incomplete datasets, inconsistent formats, or errors introduced during data collection and entry. They must meticulously review data sources, cross-check results, and ensure that statistical methodologies are correctly applied. Collaboration with data analysts, researchers, and IT teams is essential to resolve discrepancies and maintain high data standards. Staying up-to-date with industry best practices and regulatory requirements also plays a crucial role in overcoming these challenges.

What are the key skills and qualifications needed to thrive as a statistics validator, and why are they important?

To thrive as a Statistics Validator, you need a strong background in statistics, data analysis, and quality assurance, often supported by a degree in statistics, mathematics, or a related field. Familiarity with statistical software such as R, SAS, or SPSS, as well as proficiency in data validation frameworks and reporting tools, is typically required. Attention to detail, critical thinking, and strong communication skills help ensure the accuracy and clarity of validated data. These skills and qualities are crucial for maintaining data integrity, supporting decision-making, and upholding the credibility of statistical results.

What is the difference between Statistics Validator vs Data Analyst?

AspectStatistics Validator
Required CredentialsTypically a degree in statistics, mathematics, or related field; certifications like CAP or ASA are common
Work EnvironmentPrimarily office-based, working with data validation processes, quality assurance, and compliance
Employer & IndustryFinancial institutions, research organizations, government agencies, and data-driven companies
Comparison with Data Analyst

The main difference between a Statistics Validator and a Data Analyst lies in their focus. A Statistics Validator specializes in verifying the accuracy and integrity of statistical data, ensuring compliance with standards. In contrast, a Data Analyst interprets data to generate insights and support decision-making. While both roles require strong statistical knowledge, the validator emphasizes quality assurance, whereas the analyst emphasizes data interpretation and reporting.

How do you become a statistics validator?

To become a statistics validator, candidates typically need a bachelor's degree in statistics, mathematics, or a related field, along with strong analytical skills and experience with data analysis tools like Excel or statistical software. Gaining familiarity with data validation techniques and obtaining relevant certifications, such as the Certified Data Management Professional (CDMP), can enhance qualifications for this role.

Is a statistics validator job in demand?

Statistics validator roles are in demand in industries such as finance, healthcare, and research, where data accuracy is critical. These jobs often require strong analytical skills and proficiency with statistical software, and demand is expected to grow as data-driven decision-making increases across sectors.

What job categories do people searching Statistics Validator jobs in Bothell, WA look for?

The top searched job categories for Statistics Validator jobs in Bothell, WA are:

What cities near Bothell, WA are hiring for Statistics Validator jobs?

Cities near Bothell, WA with the most Statistics Validator job openings:

Infographic showing various Statistics Validator job openings in Bothell, WA as of August 2026, with employment types broken down into 84% Full Time, and 16% Contract. Highlights an 100% In-person job distribution, with an average salary of $93,519 per year, or $45 per hour.

Biostatistician

Renton, WA • Remote

micro1 AI
Software Development • 11 - 50 employees

$60 - $65/hr

Part-time

Re-posted 6 days ago


Job description

Role Title: Biostatistician


Role Type: Contractor


Location: Remote


micro1 is engaging Biostatisticians to contribute their clinical statistics expertise to a dynamic customer project focused on AI-assisted clinical research. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Author and review evaluation tasks that require deriving, reproducing, or validating statistical outputs from clinical datasets and associated tables, figures, and listings (TFLs).
  2. Apply expert judgment to assess the correctness and consistency of reported estimates, confidence intervals, p-values, analysis populations, and missing data handling in alignment with the statistical analysis plan (SAP).
  3. Identify discrepancies between statistical outputs and their narrative descriptions in clinical study reports (CSR), including subtle errors in population definitions, censoring rules, or multiplicity handling.
  4. Establish defensible ground truth for each evaluation task, documenting the derivation process to enable independent verification.
  5. Provide structured written rationales distinguishing true statistical errors from acceptable methodological alternatives, employing clear and concise communication.
  6. Collaborate with a multidisciplinary project team, providing statistical insights and feedback as needed to refine evaluation tasks and criteria.


Preferred Qualifications

  1. 5+ years as a biostatistician supporting clinical trials at a sponsor, CRO, or academic trials unit.
  2. Hands-on experience producing or quality controlling TFLs for regulatory submissions and working directly from CDISC SDTM/ADaM datasets.
  3. Solid understanding of statistical methods used in confirmatory trials, including survival analysis, mixed models, covariate adjustment, multiplicity control, and estimand and missing-data strategies under ICH E9(R1).
  4. Proficiency in SAS and/or R, with the ability to independently reproduce analyses from written specifications.
  5. Ability to interpret SAPs and ensure reported results are consistent with pre-specified analyses.
  6. Advanced degree (MSc or PhD) in Biostatistics, Statistics, or a closely related quantitative field.
  7. Nice to have: Experience as lead statistician on pivotal/registrational studies, authoring or reviewing CSR statistical sections, oncology endpoint expertise, and exposure to AI-assisted statistical review tools.