Remote Data Science Jobs

Remote data science jobs are available, but the market is selective: Remoote found 51 active searchable remote roles from 36 companies using the Data Science title filter, with 36 showing salary information, last checked June 16, 2026. Listings change quickly, so use the current results below to confirm fit before applying.

Data Science
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69 jobs found

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Shown 1-10 of 69
USFull-TimeCybersecurity AIPosted
  • Architect, build, and optimize highly scalable data platforms and pipelines supporting LLMs, neural networks, Retrieval-Augmented Generation (RAG), and AI agentic systems at Exabyte scale.
  • Design and deploy agentic workflows and agent-harnessing capabilities that enable autonomous, data-driven security solutions.
  • Remain hands-on in software development, writing elegant, production-ready code with a strong focus on performance, maintainability, testing, reliability, and rapid delivery.
  • Design fault-tolerant, cost-effective distributed systems using advanced approaches to sharding, partitioning, concurrency, and large-scale data processing.
  • Provide technical leadership across data modeling, normalization, semantic cataloging, and platform architecture for AI/ML workloads.
  • Establish and advance MLOps and DataOps standards for LLM-powered systems, including monitoring, observability, automation, and zero-touch recovery.
  • Own the complete lifecycle of critical AI and data services, from architecture and development through testing, deployment, production monitoring, and continuous improvement.
  • Partner with Data Scientists, Product Managers, and engineering teams to transform research prototypes into scalable, secure, production-grade services.
  • Drive adoption of modern AI platform technologies and continuously evaluate emerging tools, frameworks, and development practices.
  • Mentor engineers through technical workshops, architecture discussions, design reviews, and hands-on guidance.
Fully remote work within the United StatesFull-TimeSecurity & ITPosted
  • Lead, hire, coach, and develop a team of data engineers and applied data scientists.
  • Partner with cross-functional leaders to translate business objectives into technical requirements and prioritize initiatives.
  • Drive the architecture and delivery of scalable batch and streaming data pipelines and oversee the productionization of machine learning and algorithmic systems.
  • Champion data quality, reliability, privacy, compliance, governance, access controls, and cost efficiency across the data platform.
  • Develop data cataloging, documentation, and self-service capabilities that make high-quality data more accessible across the organization.
  • Guide teams through the full machine learning lifecycle, including MLOps and applied use cases such as recommendation, ranking, personalization, and classification.
United KingdomContractAI Data SciencePosted
  • Design precise, task-specific grading criteria for real-world data science deliverables such as analyses, models, dashboards, and experiment readouts.
  • Score AI-generated and human work samples against criteria with detailed written justifications for every score.
  • Apply consistent, evidence-based judgment to ensure scores are reproducible and defensible.
  • Incorporate structured feedback from senior reviewers and iterate quickly on your work.
  • Work independently and asynchronously to meet deadlines while improving AI model performance.
Based in United StatesFull-TimeHealth TechPosted
  • Own the automation of client Quarterly Business Reviews (QBRs), building AI-enabled systems and workflows that streamline preparation, reporting, insights, and commentary generation.
  • Automate recurring weekly and monthly client reporting processes in partnership with Data Engineering to reduce manual effort and improve scalability.
  • Lead the client-facing data function for RFPs by providing accurate analysis, benchmarks, and insights that support sales and proposal teams.
  • Develop and maintain a standardized framework for client SLAs, ensuring commitments are measurable, validated, and aligned with business capabilities.
  • Build and manage the core metrics framework used across Sales, Customer Success, reporting, and client communications.
  • Serve as a data consultant supporting external business narratives by validating metrics and ensuring insights are defensible.
  • Partner with clinical intelligence teams to understand healthcare outcomes and ROI models.
  • Collaborate with Sales and Customer Success leaders as an embedded analytical partner.
This is a U.S. based remote position.Full-TimeOutdoor TechnologyPosted
  • Manage, hire, and coach a team of data engineers and applied data scientists.
  • Partner with cross-functional leaders to translate business needs into technical requirements.
  • Serve as a thought partner on leveraging data, AI, and automation for product and operational improvements.
  • Drive the architecture and delivery of scalable batch and streaming pipelines.
  • Oversee productionization of ML models and algorithmic systems.
  • Champion data reliability, quality, privacy, and governance through self-service tools.
  • Improve system performance, debugging speed, and deployment velocity.
FranceFull-TimeFinance TechnologyPosted
  • Design, develop, and deploy AI and machine learning models tailored to accounting data and processes.
  • Analyze large financial datasets to identify trends, anomalies, and opportunities for automation.
  • Collaborate with accounting experts to understand domain-specific challenges and translate them into technical solutions.
  • Build data pipelines and maintain data infrastructure to support scalable analytics and AI initiatives.
  • Implement natural language processing techniques to enhance financial document analysis and extraction.
  • Evaluate and optimize existing models and algorithms to improve accuracy and efficiency.
  • Stay updated with the latest AI and data science advancements and apply relevant innovations to CapitaleTech's products.
  • Communicate complex technical concepts and insights to non-technical stakeholders effectively.
This is a U.S. based remote position.Full-TimeOutdoor TechPosted
  • Manage, hire, and coach a team of data engineers and applied data scientists.
  • Partner with cross-functional leaders to translate business needs into technical requirements and prioritize initiatives.
  • Serve as a thought partner on leveraging data, AI, and automation for internal productivity and member-facing experiences.
  • Drive the architecture and delivery of scalable batch and streaming pipelines and productionized ML models.
  • Oversee data system operations, reliability, and troubleshooting to ensure high uptime.
  • Champion data governance, quality, privacy, and compliance through improved tools and documentation.
SydneyBrisbaneHobart+13 more locationsFull-TimeCybersecurity SaaSPosted
  • Generate actionable insights from complex marketing data sets to inform channel investment and pipeline strategy.
  • Partner with marketing leaders across demand gen, partner, field, product, and brand functions.
  • Design and maintain the attribution framework including first/multi-touch and incrementality testing.
  • Develop metrics, models, and dashboards for paid, organic, content, lifecycle, partner, and field marketing.
  • Construct foundational marketing data assets and dbt models to create a governed semantic layer.
  • Provide robust self-service and agentic analytics capabilities to marketing stakeholders.
  • Conduct technical deep-dive analysis into marketing challenges like lead quality and campaign cannibalisation.
United StatesFull-TimeRisk ManagementPosted
Data Science Manager
Company:CorVel
  • Develop advanced quantitative modules using a variety of programs/software to support predictive assessments
  • Communicate analytics model behavior and results to business stakeholders
  • Carry out technical risk analysis and reliability assessments
  • Perform complex analyses, including optimization, text analytics, machine learning, and statistical analysis
  • Design algorithms that require different models and methods to be used together
  • Apply strong understanding of data science techniques and libraries to business problems
  • Manage a team of data scientists to complete all project deliverables
  • Apply engineering approaches to deliver business value
Remote (US)Full-TimeHealthcare / Data SciencePosted
  • Own analytical strategy for end-to-end marketing funnel, activation, and retention.
  • Partner with product teams as embedded analytical lead and manage product analytics roadmap.
  • Serve as executive-facing voice for funnel and retention performance diagnostics.
  • Design and lead experimentation program including test design, causal inference, and StatSig implementation.
  • Own attribution framework, including multi-touch and media mix modeling.
  • Develop dbt models, data quality standards, and semantic layer for self-service BI.
  • Lead and coach a small team using AI-native tooling for increased leverage.
Shown 1-10 of 69
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Is remote data science hiring real right now?

Yes, but it is not a huge open market. Based on Remoote job data from June 16, 2026, the current Data Science search block surfaces 51 active searchable remote jobs across 36 companies. Visible examples include Data Science & Engineering Lead, Senior Manager Data Science & Analytics, Associate Director Media Data Science, Software Engineer - Data Science, and Data Science Intern.

That mix matters because “data science” can mean hands-on modeling, analytics leadership, data engineering, experimentation, media measurement, or software work around ML systems. Read the responsibilities before assuming a listing matches your target role.

How should you screen noisy ML and analytics listings?

Start with the work, not the title. A strong data science listing should say what data you will use, which business decision the models or analysis support, what tools or languages are expected, and whether the role is individual-contributor, management, or hybrid engineering work.

  • For ML-heavy roles, look for modeling, evaluation, deployment, experiment design, or production ML ownership.
  • For analytics-heavy roles, look for metrics, dashboards, stakeholder decision support, SQL, experimentation, or product analytics.
  • For leadership roles, check whether the job is people management, technical strategy, or both.
  • For internships or early-career roles, confirm whether mentorship, scope, and required experience are realistic.

If a posting uses data science keywords but never explains the dataset, business problem, tools, or decision-making context, treat it as a noisy listing and compare it carefully with the roles below.

What can you know about salary before applying?

Salary visibility is relatively strong for this page: 36 of the 51 current Data Science-filtered roles show salary information, based on Remoote listings checked June 16, 2026. This page does not claim a salary range because compensation varies by seniority, location rules, and role type.

If pay is your first filter, compare these listings with remote job salaries before applying. If a role does not show pay, check whether the employer explains compensation later in the process and whether the location or time-zone requirements could affect the offer.

Where should you branch next?

If the current data science set is too narrow, browse the broader remote IT jobs category or compare nearby paths such as remote data analyst jobs. If you are early in your career, start with entry-level remote jobs or remote jobs without experience before spending time on senior data science listings.

Company choice also matters in a small market. Use top remote companies to identify employers with broader remote hiring activity, then return here to check whether their data roles match your skills.

Source: Remoote job listings using the current Data Science title filter, checked June 16, 2026. Counts are a point-in-time snapshot and may change as employers post, update, or close roles.

Remote data science jobs FAQ

Yes. Remoote found 51 active searchable remote jobs from 36 companies using the current Data Science title filter, checked June 16, 2026. Availability changes quickly, so confirm the current results before applying.

Data science titles are used inconsistently by employers. Some roles focus on modeling, some on analytics leadership, and some on software or data engineering around data products. Read the responsibilities, tools, and success metrics before deciding whether a listing fits your target path.

36 of the 51 current Data Science-filtered roles show salary information, based on Remoote job data from June 16, 2026. That is useful for screening, but it does not support one universal salary range because role level, location rules, and responsibilities vary.

Check whether the listing explains the data, business problem, tools, seniority, remote location rules, and interview expectations. Be cautious with vague ML or analytics titles that do not describe the actual work.

Try adjacent searches such as remote data analyst roles, broader remote IT jobs, or entry-level remote jobs if your experience level is lower. Data science supply is real but selective, so branching can help you find roles that match your skills faster.

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