Remote Data Science Jobs

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US hubs or remotely in the United StatesFull-TimeAI ProductsPosted

Manager, Data Science - AI Product

Company:Figma

Lead, support, and grow a team of data scientists and data science managers with varied strengths and expertise focused on product strategy, internal tooling, and Impact measurement
Champion data quality, accessibility, and the democratization of data across the company
Establish trust within and across teams by creating accountability and a positive work environment in partnership with other leaders in the organization
Partner with Product, Engineering, Design, Research, Sales, Marketing, or Finance to drive impact

Remote - USFull-TimePosted

Staff Data Scientist - GTM Data Science & Analytics

Company:Twilio(5001-10000 employees, Messaging, SMS, Mobile Apps)

Design, implement, and deploy predictive models and algorithms to inform sales strategies and drive measurable improvements.
Develop and maintain robust dashboards and analytics tools using SQL and business intelligence platforms such as Looker or Tableau.
Collaborate with go-to-market partners, including sales and marketing, to identify high-impact data science opportunities.
Communicate complex analyses and recommendations to technical and non-technical stakeholders, including presenting to executives.
Continuously refine metrics, measurement frameworks, and data pipelines to optimize accuracy and efficiency.

Python SQL Data Analysis+4 more

Remote - United StatesFull-TimeDigital HealthPosted

Director, Data Science

Company:Headspace(11-50 employees, Wellness, Health Care, Child Care)

Own the strategy, execution, and impact of Data Science across product, marketing, and forecasting.
Set direction for how the team supports product development, experimentation, lifecycle, growth, and business decision-making.
Ensure focus on the highest-value problems across Product, Marketing, Care, and B2B, tied to member and business outcomes.
Establish high standards across experimentation, causal inference, modeling, forecasting, segmentation, and statistical rigor.
Raise quality in hypotheses, metrics, experiment design, model evaluation, and decision recommendations.
Build durable review mechanisms and operating standards to ensure work is sound, interpretable, and decision-ready.
Lead product analytics to shape how user behavior is understood across acquisition, engagement, retention, and care journeys.
Strengthen data foundations including instrumentation, telemetry quality, validation, and experimentation readiness.
Partner cross-functionally to define measurement strategies that demonstrate care impact, outcomes, and ROI.
Build and lead a high-performing team by developing leaders, raising the talent bar, and leveraging AI to improve productivity and insight generation.

Remote, United StatesFull-TimeFinancePosted

Technical Recruiter, Research & Data Science

Company:The Voleon Group

Partner closely with Research, Data Science, and other hiring managers to support their growth efforts
Ensure we’re meeting our hiring goals and exceptionally high quality standards in a sustainable and diligent manner
Contribute to sourcing a rich pipeline of rarified technical talent
Manage the offer process and develop effective closing strategies
Own the candidate experience and make it a positive and memorable journey, even for candidates who do not join us
Maintain and improve a high-performance recruiting process tuned to delivering outstanding quality of hire in an efficient, organized manner
Develop and share institutional knowledge / insight around talent markets and effective recruiting practices and content for Voleon
Lead various projects to improve our recruiting efforts overall

IndiaFull-TimePosted

Manager, Data Science (Product Analytics)

Company:

Lead and manage a team of approximately seven data scientists, ensuring high-quality delivery of product analytics, insights, and reporting that support strategic and tactical decision-making.
Drive experimentation, measurement frameworks, and product performance tracking to evaluate user behavior, feature impact, and overall product health.
Establish and maintain standardized data processes to ensure accuracy, consistency, and reliability across analytics outputs.
Collaborate closely with product, engineering, and research teams to embed data-driven practices into product development cycles.
Contribute to the design and improvement of scalable data tools, dashboards, and visualization systems to enhance stakeholder access to insights.
Mentor and coach team members in statistical thinking, experimentation design, storytelling with data, and stakeholder communication.

Hadoop Product Analytics Data science+2 more

Remote, United StatesFull-TimeFinTechPosted

Technical Sourcer, Research & Data Science

Company:The Voleon Group

Directly source a rich pipeline of rarified technical talent across a portfolio of searches, with an initial focus on Research roles
Partner closely with sourcers, recruiters, and hiring managers to develop effective sourcing and pitching strategies
Institutionalize our talent strategies and outreach content broadly to make the whole team more effective at sourcing
Take ownership for sourcing and talent strategy-related processes and initiatives, such as new search launches and talent market evaluations, search recalibrations, referrals pushes, large systematic sourcing drives, and piloting new approaches to find and attract key talent
Own the candidate experience and make it a positive and memorable journey, even for candidates who do not join us
Maintain and improve a high-performance recruiting process tuned to delivering outstanding quality of hire in an efficient, diligent manner

U.S., Europe, and IndiaFull-TimeHealthcare AdvertisingPosted

Director, Data Science - Healthcare Advertising

Company:DeepIntent(101-250 employees, Advertising, Marketing, Health Care)

Help lead our global data science organization and advance the next generation of data innovation in healthcare advertising technology.
Oversee a team focused on advanced modeling, optimization, and AI initiatives.
Shape and execute data science initiatives that drive predictive, privacy-safe insights.
Manage and collaborate with data scientists across the U.S., Europe, and India.
Partner with executive and cross-functional leadership to translate data science capabilities into scalable, compliant, and commercially impactful solutions.
Help execute the global data science roadmap supporting DeepIntent’s mission.
Lead and mentor a distributed team of data scientists.
Develop and optimize predictive and causal models for patient and provider targeting, audience segmentation, and campaign performance measurement.
Oversee the development and deployment of machine learning models supporting real-time bidding, healthcare audience modeling, and privacy-first identity resolution.
Integrate real-world data (RWD), claims data, and digital engagement signals into scalable machine learning pipelines.
Ensure models and data science practices align with healthcare privacy standards including HIPAA and other global regulatory frameworks.
Collaborate with engineering, product, and data partnerships teams to strengthen our data infrastructure.
Implement best practices in feature engineering, reproducible research, and ML Ops.
Partner with legal and compliance teams to ensure responsible AI development and ethical use of healthcare data.
Translate complex modeling outputs into actionable insights for internal stakeholders and clients.

Python SQL Machine Learning+5 more

Anywhere in the USFull-TimeCybersecurityPosted

Staff Data Science Engineer

Company:SimSpace Corporation

Design, implement, and deploy advanced mathematical and machine-learning algorithms to support cyber-range simulations, delivering production models with documented accuracy, latency, and throughput metrics.
Develop and maintain end-to-end AI/ML pipelines (data ingestion, feature engineering, model training, validation, inference, monitoring), ensuring test coverage, reproducibility of experiments, and documented performance benchmarks.
Construct and optimize numerical methods and computational models using Python, NumPy, SciPy, Pandas, and JAX/TensorFlow/PyTorch to solve large-scale (10M+ row) data and optimization problems relevant to cyber-range operations.
Architect scalable model-serving systems in Docker/Podman/Kubernetes, achieving reliable deployments with measured service uptime of 99 percent or greater and documented resource-utilization improvements.
Develop and integrate new AI-driven cybersecurity capabilities (e.g., automated scoring engines, classification systems, reinforcement-learning-based adversary behaviors) with quantified gains in accuracy, precision/recall, or scenario realism, validated against internal evaluation datasets.
Author and maintain production-quality Python services, enforcing code standards, implementing unit/integration testing with unittest/pytest, and reducing defect rates via measurable static/dynamic analysis reports.
Lead cross-team technical initiatives, producing written design documents, conducting architecture reviews, and driving the integration of DS/AI services across engineering, product management, platform teams, and cybersecurity content engineering.
Mentor senior-level engineers and data scientists by conducting formal code reviews, mathematical model reviews, and algorithm correctness checks, with documented feedback that improves model accuracy, stability, or performance.
Apply computational mathematics methods (e.g., linear algebra, numerical optimization, differential equations, stochastic processes) to design, implement, and validate algorithms and models with documented quantitative results.
Define and establish technical standards, best practices, and design patterns for AI/ML development across the Data Science team.
Drive high-performance computing initiatives to optimize AI/ML system performance, including distributed computing and GPU acceleration strategies.

Docker Python Differential Equations+8 more

US-based candidatesFull-TimeHealthcarePosted

Data Science Engineer, Contracts

Company:Turquoise

Build and maintain software and data pipelines in support of contract management and AI-assisted workflows
Work cross-functionally with Product, Engineering, and subject matter experts to conceptualize, prototype, and build data solutions
Contribute to data capabilities around contract modeling, automated pricing, and payer policy intelligence
Draft technical design and documentation
Seek and prioritize technical and product feedback from internal customers
Iterate quickly with an eye towards value

Python SQL Django+3 more

Remote - United StatesFull-TimeConsumer TechnologyPosted

Senior Data Science Manager, Marketing

Company:Reddit(1001-5000 employees, News, Content, Social Network)

Build, lead, and mentor a high-performing team of data scientists focused on the Marketing & Business Development domain.
Serve as a thought-partner to cross-functional leaders (Product, Engineering, Design, and Marketing), communicating and shaping the roadmap and strategy for Reddit by identifying actionable and impactful insights.
Lead initiatives to quantify the true incremental lift of marketing spend and prevent budget allocation on non-incremental channels, including developing and deploying Incremental/Predictive LTV models.
Oversee the establishment of measurement frameworks (incl. effective experimental designs and/or Causal inference methods) to evaluate Brand Initiatives and ensure consistent metrics are aligned across the organization for campaign quality and ROI.
Drive the adoption of strategic and tactical recommendations based on deep product experience and data understanding to maximize our value to consumers.
Take a keen interest in the collection and quality of underlying data, including overseeing work on ETLs, reporting dashboards, and data aggregations needed for business tracking and/or ML model development.
Lead initiatives to enable analytics self-serve capabilities for cross-functional teams.

Python SQL ETL+3 more

Shown 1-10 of 49

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Why Remote Data Science Jobs Are Becoming More Popular

Landing a career that mixes serious analytics with the freedom to work from anywhere is now far more achievable than it used to be. Data science jobs sit at the center of today’s digital economy: they combine real business impact, complex problem‑solving, and advanced technical work. As more organizations rely on evidence-based decisions, the need for specialists who can translate messy, large-scale data into clear direction keeps growing faster than the supply of qualified candidates. Whether you’re an experienced professional or moving into the field from another career, success depends on understanding the core roles, the most-used tools, and how modern tech companies hire.

How Remote Data Science Work Changed the Industry

A data scientist is no longer expected to live near a major tech office. Remote data science jobs are now common across startups, scale-ups, and global enterprises. The reason is simple: modern data platforms are cloud-first. When pipelines, warehouses, and model infrastructure run in AWS or Google Cloud, performance matters more than proximity.

Remote work also delivers clear advantages:

Access to worldwide employers. You can join a fast-growing US company or a European product team while staying in your current location.
Better focus for deep work. Modeling, experimentation, and statistical analysis often improve in a quieter, controlled environment.
Less wasted time and expense. Removing the commute frees hours for learning, projects, and personal life.
More flexible pay structures. Many remote-first teams offer strong compensation that isn’t strictly pegged to local cost-of-living formulas.

With the right approach, remote work can support both rapid career development and a healthier lifestyle.

Major Roles and Specializations in the Data World

The data field has expanded into multiple tracks. Depending on whether you prefer math-heavy research, systems building, or business-facing analytics, you can target different positions. Knowing the differences helps you search for data scientist jobs (and related roles) more efficiently.

Core Data Roles

Data Scientist. Builds predictive models, runs experiments, and uses statistics and machine learning to generate forecasts and insights.
Data Engineer. Designs reliable pipelines, cleans and structures data, and ensures datasets are usable at scale.
Machine Learning Engineer. Turns models into production services, focusing on deployment, monitoring, performance, and scalability.

Related Technical Roles That Often Overlap

Companies hiring for data teams frequently need adjacent skills too, such as:

Remote software developer jobs for data-intensive products and services
Full stack developer jobs to build dashboards, internal tools, and analytics platforms
Remote DevOps jobs to maintain CI/CD and infrastructure for ML workloads
UX designer jobs to make reporting and visualizations easy to understand and act on

Expanding your search into these specializations can dramatically increase your options in the global market.

Skills That Matter Most for Remote Data Professionals

Competition is global, so your skill set needs to be both practical and demonstrable. Recruiters filling remote programming jobs want proof that you can deliver independently and write code that holds up in production. A strong portfolio typically shows capability in:

Python for analysis, automation, modeling, and deep learning workflows
SQL for extraction, transformation, and structured querying
Git/version control as a baseline for collaborative remote development
Cloud platforms (AWS, Azure, or GCP) for deploying models and data services
Communication to explain outcomes clearly to non-technical stakeholders in tools like Slack and Zoom

This blend of technical depth and remote-friendly communication makes you much more competitive for high-quality remote IT jobs.

Finding and Assessing Remote Offers the Right Way

Remote roles vary widely. Some organizations are truly remote-first, while others advertise remote options but still operate like office-based companies. When reading a job post, look for signals of real remote maturity: documented processes, clear ownership, and modern collaboration tooling (for example Jira, Notion, or Confluence).

Places to discover strong openings include:

LinkedIn and Indeed for broad discovery and alert-based searching
Remote-focused job boards that often include salary transparency and clearer expectations
Company career pages where many large employers list remote-eligible roles directly
GitHub and Kaggle where open-source contributions and competition work can attract recruiter outreach

The quality of a company’s remote culture is often as important as salary, because it affects onboarding, growth, and day-to-day productivity.

Applications and Interview Prep for Remote Data Science Jobs

Hiring for remote roles often includes multiple steps: automated screening, technical tasks, live coding, and behavioral interviews. To improve your hit rate:

Customize your CV for each role. Include key terms (Python, SQL, ML), but also demonstrate self-management and communication.
Prepare for SQL evaluations. Expect joins, window functions, and performance/optimization questions.
Showcase a clean portfolio. A well-documented GitHub project can outweigh a long list of certificates.
Match your experience to their stack. If you’re applying to React JS remote jobs or Java remote jobs within data-heavy teams, highlight relevant projects that prove competence.

These steps help recruiters quickly see you as dependable, capable, and ready to contribute remotely.

Build Your Career in Data—Remotely

A flexible, well-paid data career is built through continuous improvement. As Generative AI, automated ML, and modern data platforms evolve, the professionals who keep learning remain in demand. Start by comparing your current strengths with real job requirements, strengthening your portfolio, and improving your online presence. Whether your goal is a specialized ML engineering path or broader data science jobs, remote opportunities are available worldwide—now is a strong time to pursue them.