650 Remote Data Scientist Jobs (October 2026)
Remote data scientist openings in October 2026 can span product analytics, machine learning, experimentation, forecasting, and decision science work, with many roles asking for Python, SQL, statistics, and clear communication with business or product teams. Candidates should expect job descriptions to vary by seniority, from hands-on modeling and dashboard analysis to research-heavy or stakeholder-facing data roles. Create an account to explore the full job feed and auto-apply with LiftmyCV AI Agent.
NLP / LLM Data Scientist
RemoteDandelion Health is seeking a skilled NLP / LLM Data Scientist to leverage clinical and text-based data for healthcare improvements. This role involves building and utilizing large language models and ML-based approaches for data abstraction…
Posted 1 week ago
Principal Data Scientist (NLP + Applied AI)
RemoteWiley is seeking a Principal Data Scientist with expertise in NLP and Applied AI to join a small team responsible for building systems that transform scientific literature into actionable research intelligence. The successful candidate will own content…
Posted 2 weeks ago
Research Data Scientist
RemoteInnodata Inc. is seeking a Research Data Scientist specializing in Generative AI and Large Language Models (LLMs) to join their AI/LLM Delivery Unit. The ideal candidate will possess strong research and analytical capabilities, hands-on AI/ML expertise,…
Posted 2 weeks ago
Junior Data Scientist
RemoteTensorOps is seeking a Junior Data Scientist to join its fully remote team in Pakistan. This entry-level role offers an incredible opportunity for recent graduates or those with limited experience to develop skills in data analysis and AI technologies. You…
Posted 6 days ago
Senior Data Scientist (NLP + Applied AI)
RemoteWiley is seeking a Senior Data Scientist with a strong background in NLP and applied AI to develop production-level systems for research intelligence from vast scientific corpora. This role invites an innovative thinker who can drive the end-to-end…
Posted 2 weeks ago
Lead Data Scientist
RemoteFacet Wealth is seeking a Lead Data Scientist to join their innovative team, focusing on delivering personalized financial advice through a unique blend of human expertise and cutting-edge technology. The successful candidate will take…
Posted today
Lead Data Scientist
RemoteAbout ParetoHealth ParetoHealth is redefining the way employers fund healthcare. As the largest and fastest-growing benefits captive in the United States, we help thousands of small and midsize employers take control of healthcare costs…
Posted 1 week ago
Staff Data Scientist, Growth
RemoteAlpaca is a leading global provider of agent-first brokerage infrastructure, catering to various financial institutions with robust APIs. They are seeking a Staff Data Scientist, Growth to enhance customer measurements and growth across their…
Posted 6 days ago
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Principal Data Scientist - Deep Learning
RemoteWHO WE ARE At Jampp, we’re on the mission of playing a leading role enabling the mobile app economy to grow. How? We build technology to support the most ambitious companies - from gaming to commerce pioneers - to propel the reach of…
Posted 2 weeks ago
Principal Data Scientist
RemoteJob Requisition ID # 26WD101222 Position Overview With the current footprint in construction projects and recent acquisitions, Autodesk is well equipped to revolutionize construction industry. The team is responsible for ensuring that…
Posted 1 week ago
Principal Data Scientist
RemoteForward Financing, a financial technology firm based in Boston, is looking for a Principal Data Scientist to lead their data science initiatives. In this role, you'll set the technical vision, mentor team members, and deliver impactful analytical…
Posted 1 week ago
Principal Data Scientist
RemoteAbout G2 - The Company G2 is the world's largest and most trusted software marketplace. When you join G2, you’re joining the industry’s leading team that helps businesses reach their peak potential by powering decisions and strategies with…
Posted 2 weeks ago
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Remote Data Scientist Salary Data in October 2026
This section summarizes salary information for Remote Data Scientist jobs, including postings across data science, machine learning, analytics, and statistical modeling roles. It uses 650+ active postings as context where salary details are available.
Average Salary
$130k
$166k
$191k
25th
50th
75th
Based on 650 roles currently tracked by LiftmyCV. Last updated on Oct 2, 2026
Salary Distribution
Based on 650 roles currently tracked by LiftmyCV. Last updated on Oct 2, 2026
| Experience Level | 25th Percentile | Median (50th) | 75th Percentile | Sample Size |
|---|---|---|---|---|
| Overall | $130,212.5 | $166,400 | $190,625 | 650 |
| Entry-Level | $62,560 | $117,500 | $166,400 | 27 |
| Mid-Level | $142,064.5 | $163,500 | $184,300 | 41 |
| Senior-Level | $148,650 | $180,875 | $209,398.63 | 64 |
"Remote data scientist hiring in 2026 tends to reward candidates who can show more than model-building ability. Recruiters often look for evidence of clean experimentation, stakeholder communication, production-aware workflows, and comfort working across async teams. The strongest remote profiles usually connect Python, SQL, statistics, and business judgment to decisions a product, growth, finance, or operations team can actually use without constant hand-holding."
Marina's Market Take
Senior HR Leader & Lead Tech Recruiter
How to Land a Remote Data Scientist Role in 2026
Remote data scientist roles in 2026 usually reward candidates who can show clear ownership of analysis, modeling, and business-facing decisions without relying on in-office context. Your application should connect technical work to a specific lane, such as product analytics, experimentation, machine learning, forecasting, risk modeling, customer behavior analysis, or operations analytics. A remote data scientist profile reads better when it shows how you scoped messy questions, worked with stakeholders across time zones, and turned Python, SQL, notebooks, dashboards, or model outputs into decisions people could use.
Position your experience around the kind of data science work you want next. For product data scientist roles, emphasize experiment design, metric definition, funnel analysis, retention work, and communication with product managers. For machine learning data scientist roles, show feature work, model evaluation, deployment handoffs, monitoring, and collaboration with engineering. For analytics-heavy roles, lead with SQL depth, statistical reasoning, dashboard interpretation, and examples where your analysis changed roadmap, pricing, support, growth, or operations decisions.
- Show remote operating habits: mention async documentation, written analysis summaries, stakeholder updates, reproducible notebooks, and clear handoffs to engineering, product, or leadership.
- Match the role’s data stack: prioritize listings that align with your actual tools, such as Python, R, SQL, dbt, Snowflake, BigQuery, Spark, Tableau, Looker, notebooks, experimentation platforms, or cloud ML workflows.
- Use project evidence carefully: highlight one or two projects with the dataset problem, method, decision, and result. A churn model, marketplace experiment, fraud signal, or forecasting workflow is stronger than a long list of algorithms.
- Filter remote roles by collaboration style: fully remote, hybrid-remote, async-friendly, distributed team, and time-zone expectations can change the daily work of a data scientist role as much as the modeling requirements.
LiftmyCV helps you find remote data scientist jobs that match your skills, experience, and preferred work style, then auto-apply to relevant roles faster.
Required Skills
Resume Tips
For remote data scientist roles, your resume should show that you can turn messy data into decisions without needing constant in-person context. Lead with Python or R, SQL, statistical modeling, experiment design, machine learning, and cloud or notebook tools such as AWS, GCP, Databricks, Snowflake, BigQuery, Jupyter, Git, Airflow, or dbt when they match your work. If you have experience with stakeholder-facing analysis, remote collaboration, product analytics, forecasting, NLP, recommendation systems, or A/B testing, put that near the top rather than burying it under coursework.
Cut vague lines about being “data-driven” or “passionate about insights.” Also remove long lists of libraries you’ve barely used. A remote data scientist resume is stronger when each project explains the dataset, method, business question, and result. Certifications can help if they’re directly relevant, such as AWS Machine Learning, Google Professional Data Engineer, or Databricks credentials, but they shouldn’t replace applied project evidence.
Weak bullet: “Built machine learning models to improve customer analytics.”
Strong bullet: “Built a churn model in Python using SQL-extracted product usage data, improving retention targeting by identifying high-risk customer segments for the lifecycle team.”
LiftmyCV helps you create an ATS-friendly data scientist resume tailored to each job, so your skills and experience better match what employers are looking for.
How to Prepare for Interviews
Remote data scientist interviews in 2026 usually test how you reason with messy data, explain tradeoffs, and work without constant in-person context. Prepare a few project stories that connect a model or analysis to a measurable result, such as reducing churn, improving forecast accuracy, or prioritizing customer segments. Be ready to walk through the dataset, assumptions, feature choices, validation method, and what you would change if the model failed after launch.
Expect formats such as a Python or SQL technical screen, a take-home analysis, a live statistics question, or a product case like, How would you measure whether a new recommendation feature is working? For remote roles, practice narrating your thought process clearly over video while using a shared notebook, dashboard, or whiteboard tool. If you have a portfolio, choose two or three projects with clean charts, concise methodology notes, and plain-English business takeaways. Review experiment design, regression, classification metrics, data leakage, stakeholder communication, and how you handle ambiguous requests from product, analytics, or engineering partners.