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Remote Data Scientist Machine Learning Jobs in Everett, WA

Zortech Solutions

MLOps Engineer

Bellevue, WA · Remote

Bellevue, WA Remote Work100% Primary SkillsAWS Cloud Formation * MLOps Engineer to work on AWS ... machine learning / data science. * 4-6 years of strong experience with the following machine ...

Block

Staff Product Data Scientist, Lending

Seattle, WA · On-site +1

Today, Cash App has thousands of employees working globally across office and remote locations ... The Role The Data Science team at Block turns unique customer and product data into decisions that ...

Axon

Senior Agentic AI Research Scientist

Seattle, WA · On-site +1

$112K - $142K/yr

As a research scientist at Axon you will play a crucial role in developing AI solutions that ... machine learning and gen-AI techniques for cloud and devices from multimodal data sources ...

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All JobsData Scientist Machine Learning JobsRemote Data Scientist Machine Learning JobsRemote Data Scientist Machine Learning Jobs in Everett, WA

Remote Data Scientist Machine Learning information

See Everett, WA salary details

$41.4K

$135.6K

$217.1K

How much do remote data scientist machine learning jobs pay per year?

As of Aug 21, 2026, the average yearly pay for remote data scientist machine learning in Everett, WA is $135,586.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,800.00 and $150,200.00 per year, depending on experience, location, and employer.

What does a remote data scientist specializing in machine learning do?

A Remote Data Scientist specializing in Machine Learning uses advanced statistical techniques and programming skills to analyze large datasets and build predictive models, all while working from a remote location. They design, develop, and deploy machine learning algorithms to solve business problems, such as forecasting trends or automating processes. Their work often involves data cleaning, feature engineering, model selection, and collaborating with cross-functional teams to integrate these models into products or services. Remote data scientists typically use tools like Python, R, and cloud-based platforms to perform their tasks efficiently.

How do remote data scientists specializing in machine learning typically collaborate with cross-functional teams?

Remote data scientists in machine learning often work closely with product managers, engineers, and business analysts through virtual meetings, collaborative platforms, and shared documentation tools. They regularly participate in sprint planning, code reviews, and brainstorming sessions to ensure alignment with project goals. Effective communication and proactive updates are essential for overcoming the challenges of remote collaboration and maintaining project momentum. Building strong relationships with team members across different time zones helps foster innovation and ensures that machine learning solutions are well-integrated into broader business objectives.

What are the key skills and qualifications needed to thrive as a remote data scientist specializing in machine learning?

To excel as a Remote Data Scientist in Machine Learning, you need a solid background in statistics, programming (typically Python or R), and a degree in computer science, mathematics, or a related field. Familiarity with tools and frameworks such as TensorFlow, scikit-learn, PyTorch, and experience with cloud platforms like AWS or Azure are often required, along with relevant certifications. Strong problem-solving skills, effective communication, and the ability to work independently are crucial soft skills for remote collaboration and translating insights for diverse stakeholders. These competencies ensure the development of robust models, clear communication of findings, and successful project delivery in a distributed work environment.

What is the difference between Remote Data Scientist Machine Learning vs Remote Data Scientist?

AspectRemote Data Scientist Machine LearningRemote Data Scientist
Required CredentialsMaster's or PhD in Data Science, Computer Science, or related field; experience with ML frameworksSimilar educational background; may focus more on statistical analysis and data visualization
Work EnvironmentPrimarily involves developing ML models, coding in Python/R, and deploying algorithmsFocuses on data analysis, reporting, and insights generation, often with less emphasis on ML deployment
Employer & Industry UsageUsed in tech, finance, healthcare for predictive modeling and automationCommon across various industries for data analysis and business intelligence

While both roles require strong analytical skills and similar educational backgrounds, Remote Data Scientist Machine Learning specializes in developing and deploying machine learning models, whereas Remote Data Scientist focuses more on data analysis and reporting. The ML role often involves coding and algorithm development, making it more technical in nature.

What are popular job titles related to Remote Data Scientist Machine Learning jobs in Everett, WA?

For Remote Data Scientist Machine Learning jobs in Everett, WA, the most frequently searched job titles are:

What job categories do people searching Remote Data Scientist Machine Learning jobs in Everett, WA look for?

The top searched job categories for Remote Data Scientist Machine Learning jobs in Everett, WA are:

What cities near Everett, WA are hiring for Remote Data Scientist Machine Learning jobs?

Cities near Everett, WA with the most Remote Data Scientist Machine Learning job openings:

Remote Data Scientist Machine Learning jobs near you

Senior Product Manager, AI & Data Science Products

Crunchbase

Seattle, WA • On-site, Remote

$144K - $190K/yr

Full-time

Posted 9 days ago

Job description

About Crunchbase
Crunchbase is a predictive solution that provides intelligence on private companies, powered by the unique combination of live private company data, AI, and market activity from over 80 million users. We predict private market movements that matter to help investors, dealmakers, and analysts make the right decisions.
We are committed to fostering a positive, diverse, and inclusive culture by hiring for potential and embracing individuals with diverse perspectives, backgrounds, experiences, and skill sets. We value transparency and openness, believing that an inclusive environment strengthens our teams and enhances our products.
About the Role
The Senior Product Manager, AI & Data Science Products owns Crunchbase's customer-facing AI data layer: proprietary data and intelligence generated from foundational data using AI and machine learning.
The primary charter is to identify high-value opportunities for new model-derived data, validate their value with customers, and take successful products from experimentation through scaled adoption.
Success is measured by three outcomes:
  • New differentiated data: Create proprietary intelligence that Crunchbase could not practically produce through collection alone.
  • Higher customer value: Help customers discover, understand, evaluate, and prioritize their private market jobs more effectively.
  • Revenue and adoption: Turn valuable AI data into measurable usage, retention, expansion, and monetization opportunities.
What You'll Do
AI & Data Science Product Strategy
  • Own the strategy and roadmap for Crunchbase's customer-facing AI data layer.
  • Identify high-value opportunities for new predictions, classifications, signals, and insights that improve customer decisions.
  • Build a differentiated portfolio of AI data products rather than isolated AI features.
  • Partner with Foundational Data to determine when customer needs are best addressed through collected, acquired, inferred, predicted, or generated data.
Customer Discovery & Product Development
  • Work directly with customers to identify where new or better data can materially improve their workflows and decisions.
  • Rapidly test new AI data concepts, validate customer value, and scale successful products.
  • Define how model-derived data, including confidence and uncertainty, should be presented to customers.
  • Partner with Design, Engineering, and Data Science to deliver AI data across Crunchbase products, APIs, MCP, and data delivery experiences.
Quality & Product Economics
  • Define quality standards and evaluation frameworks for model-derived data in partnership with Data Science.
  • Determine when an AI data product is sufficiently reliable for scaled customer use.
  • Balance customer value, coverage, accuracy, freshness, and generation cost.
  • Monitor product and data performance and continuously improve quality based on customer feedback and observed outcomes.
Adoption & Monetization
  • Drive adoption of AI data products across Crunchbase's customer experiences and distribution channels.
  • Partner with Go-to-Market on positioning, customer education, and launch strategy.
  • Partner with Pricing and Packaging and Sales to identify monetization opportunities.
  • Measure adoption, retention, expansion, revenue, and customer outcomes to determine which products to scale, improve, or retire.
What We're Looking For
  • Strong product judgment across customer discovery, strategy, prioritization, experimentation, and tradeoffs.
  • Strong understanding of data products and how customers derive value from proprietary data and insights.
  • Practical understanding of modern machine learning and AI capabilities and limitations.
  • Working knowledge of applied data science and machine learning.
  • Ability to translate product requirements for Data Science and Engineering teams.
  • Familiarity with model evaluation concepts such as precision, recall, confidence, and model drift.
  • Ability to reason about probabilistic and imperfect data and define appropriate quality thresholds.
  • Strong analytical skills and ability to balance customer value, quality, coverage, cost, and speed.
  • Excellent customer discovery, communication, and cross-functional leadership skills.
Education and Experience
  • 3+ years of Product Management, Data Product Management, AI/ML Product Management, or comparable experience.
  • Experience owning customer-facing data science products from problem definition through launch and ongoing monitoring.
  • Experience partnering closely with Data Science and Engineering teams.
  • Demonstrated experience taking products from customer discovery and experimentation through scaled adoption.
  • Ability to define quality criteria that reflect customer needs and make informed quality and coverage tradeoffs.
  • Experience with B2B SaaS, data products, APIs, intelligence platforms, or commercializing differentiated data preferred.
Success in This Role Looks Like
  • Crunchbase launches differentiated AI data products that customers value and competitors cannot easily replicate.
  • AI creates valuable intelligence and coverage that would be impractical to produce through traditional data collection alone.
  • Customers adopt these products because they improve real workflows and decisions.
  • AI data products contribute measurably to adoption, retention, expansion, and revenue while meeting appropriate quality and trust standards.
Non-Goals
  • This is not an internal AI tooling or general AI feature role.
  • This is not ownership of foundational data collection, sourcing, or operations.
  • This is not ML research or data generation for its own sake. AI data must solve meaningful customer problems and create measurable value.
Interview Process
We use a structured interview process so every conversation has a distinct purpose and candidates are evaluated consistently against role-relevant evidence.
  1. Recruiter Prescreen - qualification and mutual fit. Confirm role basics, motivation, logistics, compensation alignment, and candidate priorities.
  2. Interview Round 1 - hiring-manager evidence interview. Evaluate the capabilities most predictive of success using consistent behavioral questions and anchored scoring.
  3. Interview Round 2 - work sample or functional deep dive. Explore the role's most important on-the-job capabilities through a realistic, time-bounded discussion or exercise.
  4. Final Round - decision-gap interview. Assess any unresolved evidence required for a confident decision, such as cross-functional collaboration, judgment, leadership, or values in practice.

Department Product Role Product Management Locations Multiple locations Remote status Fully Remote Employment type Full-time

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