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Remote Full Stack Data Scientist Jobs in Phoenix, AZ

Mondo

Data Scientist

Phoenix, AZ · Remote

$65 - $75/hr

Senior Data Scientist , Remote. Start date is ASAP for this 12 Month Contract position. Job Title ... Contribute across the full development lifecycle, from experimentation through production release ...

Provn

Full Stack AI Engineer, Security

Scottsdale, AZ · Remote

$130K - $170K/yr

About the job Full Stack AI Engineer - Security Arrivia Remote-US Full-time Paste this url into ... and data leakage, and owning the pipelines behind them from data ingestion through training ...

GitKraken

Applied Data Scientist

Scottsdale, AZ · Remote

... Data Scientist who can take an idea from concept to production. Sometimes that idea will come from ... Ability to work across the stack, not just in notebooks * Strong product judgment and a bias toward ...

GitKraken

Applied Data Scientist

Scottsdale, AZ · Remote

... Data Scientist who can take an idea from concept to production. Sometimes that idea will come from ... Ability to work across the stack, not just in notebooks * Strong product judgment and a bias toward ...

Viasat, Inc.

Data Engineer

Tempe, AZ · On-site +1

$174K - $261K/yr

... full stack developer using one of the following: Java, Python, or Scala Salary range $140,500.00 ... Science, Computer Engineering, Software Engineering, Electrical Engineering, Math, Physics or a ...

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Remote Full Stack Data Scientist information

See Phoenix, AZ salary details

$45.7K

$163.8K

$241.8K

How much do remote full stack data scientist jobs pay per year?

As of Aug 7, 2026, the average yearly pay for remote full stack data scientist in Phoenix, AZ is $163,848.00, according to ZipRecruiter salary data. Most workers in this role earn between $132,600.00 and $168,800.00 per year, depending on experience, location, and employer.

What is a remote full stack data scientist?

A Remote Full Stack Data Scientist is a professional who works from any location to handle the end-to-end process of data science projects. This includes collecting and cleaning data, developing and training machine learning models, deploying solutions into production, and sometimes building user-facing applications. They are skilled in both back-end (data engineering, modeling) and front-end (visualization, application development) tasks. Remote Full Stack Data Scientists often collaborate with distributed teams using digital communication and project management tools. Their broad skill set allows them to manage the entire data pipeline and deliver actionable insights or products remotely.

How do remote full stack data scientists typically collaborate with cross-functional teams in a virtual environment?

Remote Full Stack Data Scientists frequently work with product managers, engineers, and business analysts using collaboration tools such as Slack, Zoom, and project management platforms like Jira or Trello. Regular virtual meetings, clear documentation, and version control systems (like Git) help ensure transparency and alignment across teams. Effective communication and proactive updates are essential to keep projects on track and to integrate data-driven insights into the broader product development process.

What are the key skills and qualifications needed to thrive as a remote full stack data scientist?

To thrive as a Remote Full Stack Data Scientist, you need a strong foundation in statistics, machine learning, data engineering, and programming languages like Python or R, usually supported by a relevant degree in computer science, statistics, or a related field. Familiarity with tools and frameworks such as TensorFlow, PyTorch, SQL databases, cloud platforms (AWS, Azure, GCP), and experience with version control systems like Git are typically required. Excellent problem-solving, communication, and self-motivation skills are crucial for collaborating remotely and translating complex data insights to stakeholders. These combined abilities ensure effective end-to-end data solutions, seamless remote teamwork, and impactful business outcomes.
What are popular job titles related to Remote Full Stack Data Scientist jobs in Phoenix, AZ? For Remote Full Stack Data Scientist jobs in Phoenix, AZ, the most frequently searched job titles are:
What job categories do people searching Remote Full Stack Data Scientist jobs in Phoenix, AZ look for? The top searched job categories for Remote Full Stack Data Scientist jobs in Phoenix, AZ are:
What cities near Phoenix, AZ are hiring for Remote Full Stack Data Scientist jobs? Cities near Phoenix, AZ with the most Remote Full Stack Data Scientist job openings:
Remote Full Stack Data Scientist jobs near you

Data Scientist

Mondo

Phoenix, AZ • Remote

$65 - $75/hr

Contractor

Medical, Dental, Vision, Retirement

Posted 22 days ago

Job description

Apply now: Senior Data Scientist , Remote. Start date is ASAP for this 12 Month Contract position.

Job Title: Senior Data ScientistLocation/Type: Remote (Candidate must reside in Pacific, Mountain, or Central time zone. Eastern time zone and international candidates will not be considered.)Start Date: ASAPDuration: Contract, 6  Months (extension likely)Compensation Range: $65/hr to $75/hrBenefits: Eligible for Health, Dental, Vision, and 401KVisa Sponsorship: Not eligible for visa sponsorship

Job Description:The client is seeking a Data Scientist with deep expertise in Generative AI, agentic architectures, and MLOps to design, build, and scale end to end AI solutions while embedding Responsible AI practices across the full development lifecycle. This role requires hands on MLOps maturity, not just model building, the candidate will own how models move from experimentation into production and stay reliable once they get there.

Job Summary:

  • Design and deploy end to end RAG solutions and autonomous AI agents in cloud and enterprise environments
  • Build and scale machine learning and AI models on cloud platforms, primarily AWS or Azure
  • Develop and maintain MLOps pipelines to support model deployment, monitoring, versioning, and governance
  • Own CI/CD for ML workflows, including automated retraining, model registry management, and rollback procedures
  • Implement model monitoring for drift, performance degradation, and data quality issues in production
  • Apply statistical modeling techniques to solve complex business problems
  • Collaborate with stakeholders across the organization to translate requirements into scalable AI solutions
  • Embed Responsible AI practices across model development, deployment, and governance workflows
  • Contribute across the full development lifecycle, from experimentation through production release

Requirements:

Must Haves:

  • Location: candidate must be based in Pacific, Mountain, or Central time zone. This is a hard requirement, not a preference.
  • Minimum 4 years of experience working specifically as a Data Scientist (title and scope must match, not adjacent titles like Data Analyst or ML Engineer alone)
  • Must currently or most recently hold a Data Scientist title (Data Scientist, Senior Data Scientist, Staff Data Scientist, Principal Data Scientist, etc.). Candidates whose current or most recent role carries a different title (Data Analyst, ML Engineer, Analytics Engineer, etc.)
  • Minimum 3 years of hands on MLOps experience, specifically model deployment, monitoring, and lifecycle management in production environments (not just model development or notebooks)
  • Direct experience with at least one MLOps tooling stack such as MLflow, Kubeflow, SageMaker Pipelines, or Azure ML Pipelines
  • Master's degree in a STEM field
  • 4 years of proficiency in SQL
  • 4 years of proficiency in Python
  • Hands on experience with AWS or Azure cloud platforms
  • Proficiency with Git for version control
  • Strong communication skills with demonstrated ability to work cross functionally with stakeholders

Nice to Haves:

  • Experience with Snowflake for data warehousing and analytics
  • Hands on experience with AWS specifically, in addition to general cloud proficiency
  • Startup or fast paced environment mindset with comfort navigating ambiguity
  • Active personal use of AI tools and familiarity with the evolving AI landscape