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Statistics Validator Jobs in Bothell, WA (NOW HIRING)

Socure

Data Scientist ll - Digital Intelligence

Seattle, WA · On-site +1

$140K - $170K/yr

Solid understanding of supervised learning, unsupervised learning, feature engineering, model evaluation, statistical validation, and experiment analysis. * Ability to work with noisy data, imperfect ...

Okta

Sr. Manager, Marketing Analytics

Bellevue, WA · On-site

$170K - $233K/yr

Design and support marketing experiments with rigorous testing and statistical validation * Mentor and provide analytical guidance to junior analysts, and establish best practices for the broader ...

micro1 AI

Director of Biostatistics

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 ...

micro1 AI

Director of Biostatistics

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 ...

micro1 AI

Director of Biostatistics

Seattle, 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 ...

micro1 AI

Director of Biostatistics

Bellevue, 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 ...

YO AI Labs

Biostatistician Expert - Remote

Seattle, WA · Remote

$60 - $70/hr

Analyze and validate clinical trial data. * Review and interpret Statistical Analysis Plans (SAPs). * Produce and quality-check Tables, Figures & Listings (TFLs). * Review Clinical Study Reports ...

YO AI Labs

Biostatistician Expert - Remote

Seattle, WA · Remote

$60 - $70/hr

Analyze and validate clinical trial data. * Review and interpret Statistical Analysis Plans (SAPs). * Produce and quality-check Tables, Figures & Listings (TFLs). * Review Clinical Study Reports ...

micro1 AI

Biostatistician

Bellevue, 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 ...

micro1 AI

Biostatistician

Seattle, 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 ...

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Showing results 21-40

All JobsStatistics Validator JobsStatistics Validator Jobs in Bothell, WA

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:

Statistics Validator jobs near you
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.

Data Scientist ll - Digital Intelligence

Seattle, WA • On-site, Remote

Socure
Software Development • 501 - 1,000 employees
Learn more about Socureexternal

$140K - $170K/yr

Full-time

Re-posted 24 days ago

Key responsibilities

  • Develop machine learning features, models, and analytical methods for device, network, browser, mobile, session, and behavioral intelligence.

  • Work on scoped fraud and identity risk problems by analyzing data quality, labels, telemetry coverage, and product tradeoffs.

  • Partner with engineering, product, and risk teams to clarify requirements, prepare data, implement features, and support production rollout.

Job description

Why Socure?
Socure is building the identity trust infrastructure for the digital economy - verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won't be your place. If you want to help build the future of identity with a team that holds a high bar for itself - keep reading.
Job Summary:
Socure is the leading provider of digital identity verification and fraud prevention solutions, using AI and machine learning to power accurate identity trust decisions. Our mission is to eliminate identity fraud and ensure online trust across industries.
We are seeking a Data Scientist II to join our Digital Intelligence team. In this role, you will develop machine learning features, analytical methods, and production-oriented risk signals using device, network, browser, mobile, API, session, and behavioral telemetry.
This is a hands-on role for a data scientist who can independently deliver well-scoped projects, work with complex and noisy data, and partner with engineering, product, and risk teams to improve fraud detection, identity confidence, and customer outcomes. You will deepen your expertise in Digital Intelligence while contributing to models and signals used in real-world production decisions.
Job Responsibilities:
  • Develop machine learning features, models, and analytical methods for device, network, browser, mobile, session, and behavioral intelligence.
  • Work on scoped fraud and identity risk problems where data quality, labels, telemetry coverage, and product tradeoffs need careful analysis.
  • Build features from large-scale, high-cardinality, sparse, noisy, and platform-dependent telemetry.
  • Analyze signal patterns such as spoofing, emulator behavior, automation, proxy/VPN usage, low-entropy fingerprints, telemetry gaps, and device or session fragmentation.
  • Design and execute validation analyses, including train/test splits, holdout checks, leakage review, drift assessment, customer impact analysis, and feature stability review.
  • Use supervised, unsupervised, statistical, and heuristic approaches to identify durable fraud and identity risk signals.
  • Investigate imperfect labels, delayed outcomes, instrumentation gaps, and changing fraud patterns to distinguish useful signal from data artifacts.
  • Partner with senior data scientists, engineering, product, risk, and platform teams to clarify requirements, prepare data, implement features, and support production rollout.
  • Contribute to model documentation, feature definitions, explainability materials, dashboards, and production-readiness reviews.
  • Communicate methods, assumptions, findings, limitations, and recommendations clearly to technical and cross-functional stakeholders.
  • Support junior data scientists and analysts through code review, analytical feedback, and sharing effective modeling and validation practices.

Job Requirements:
  • Bachelor's, Master's, or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or a related quantitative field, or equivalent practical experience.
  • 5+ years of experience in data science, applied machine learning, statistical modeling, analytics engineering, or a related technical role.
  • Experience building, evaluating, and improving machine learning models, features, analytical pipelines, or risk signals.
  • Strong SQL skills and experience working with large-scale, complex datasets.
  • Strong proficiency in Python and experience with data science libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, PyTorch, or similar.
  • Experience with distributed data processing tools such as Spark, PySpark, Databricks, or equivalent frameworks.
  • Solid understanding of supervised learning, unsupervised learning, feature engineering, model evaluation, statistical validation, and experiment analysis.
  • Ability to work with noisy data, imperfect labels, missing values, instrumentation gaps, and changing data distributions.
  • Strong analytical judgment across data quality, feature design, model selection, explainability, and business impact.
  • Experience collaborating with engineering, product, analytics, or risk teams to move data science work toward production or operational use.
  • Clear communication skills, including the ability to explain technical work, assumptions, tradeoffs, and results to non-specialist stakeholders.
  • Ability to operate independently on defined problem areas while seeking guidance appropriately on ambiguous or high-risk decisions.

Preferred Qualifications:
  • Background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain.
  • Experience with device intelligence, browser/mobile fingerprinting, behavioral biometrics, network intelligence, VPN/proxy detection, or telemetry signal processing.
  • Experience developing features from high-cardinality categorical data using techniques such as aggregation, frequency encoding, target encoding, embeddings, graph features, or representation learning.
  • Familiarity with production ML workflows, model monitoring, feature monitoring, or batch and near-real-time decisioning systems.
  • Experience with dashboarding, model explainability, feature documentation, or customer-impact analysis.
  • Interest in adversarial behavior, fraud patterns, telemetry quality, and applied ML systems that operate in real-world production environments.

What You'll Gain
You will work on meaningful data science problems in fraud prevention and identity verification, using high-scale Digital Intelligence telemetry to build features and risk signals that contribute to real-world production decisions.
You will gain deeper experience with device, network, browser, mobile, session, and behavioral intelligence while working closely with senior data scientists, engineering, product, and risk partners. This role offers the opportunity to grow from independently delivering scoped modeling projects toward owning broader workstreams and developing Senior-level technical judgment over time.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process-including interview or onboarding support-please reach out to your Socure recruiting partner directly.
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