Browse remote data scientist positions from companies actively hiring: analytics, forecasting, experimentation, and applied ML roles.
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Posted 9 hours ago
Nebius
Amsterdam, Netherlands; Remote - Europe
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Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers...
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Constructor is the next-generation platform for search and discovery in ecommerce, built to explicitly optimize for metrics like revenue, conversion r...
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Posted 1 day ago
Constructor
Remote - Brazil
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Constructor is the next-generation platform for search and discovery in ecommerce, built to explicitly optimize for metrics like revenue, conversion r...
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Why Faculty? We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we’ve worked wi...
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This is a fully remote opportunity with hybrid available to those local to Boston. Gradient AI is the decision-intelligence partner for the insurance...
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Why Faculty? We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we’ve worked wi...
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Posted 1 week ago
Fireworks AI
San Mateo
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Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows,...
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Posted 1 week ago
CodeRabbit
San Francisco
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CodeRabbit is the governance and control layer for agentic software development, helping engineering teams trust the code they ship. AI can generate c...
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Posted 1 week ago
Qventus
Remote, Canada · also open in 1 other location
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On this journey for over 12 years, Qventus is leading the transformation of healthcare. We enable hospitals to focus on what matters most: patient car...
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On this journey for over 12 years, Qventus is leading the transformation of healthcare. We enable hospitals to focus on what matters most: patient car...
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How it works
Career context
Data scientist roles vary enormously from company to company. One company's data scientist writes SQL dashboards. Another's builds production ML models. Another's runs statistical experiments. Before applying, always check what the role actually involves. Mismatched hires waste months for both sides. The best data scientists are clear about which type of work they do best, and they target roles accordingly.
What gets you hired isn't your algorithm knowledge; it's your ability to describe the business impact of your work. Hiring managers want to know: what question were you answering, what was the data environment, what did you find, and what changed as a result? A churn model that reduced churn by 12% is more compelling than a list of scikit-learn classes. Frame your experience around outcomes, not methods.
Comparing offers? Our guide Data scientist salary in 2026 shows the official US median, total pay at tech companies and pay by level.
Why RemoteAI
Some roles are posted here by the company, others link to the company's own job page. Closed roles are marked as closed, and there are no recruiter markups.
Roles are filtered to AI work. Each listing shows the company and links to its own job page.
No recruiter in the middle. Your application goes to the company making the decision, here or on its own job page. No markups.
Your profile, browsing and up to 20 applications a day are free. No placement fee, no share of your salary, no exclusive contract.
Jobs posted on RemoteAI show a pay range. Listings from company career pages show it when the company publishes one.
Remote and remote-friendly roles. Some require timezone overlap or a country, and the listing says where you can work from.
Track all your applications, messages, and interviews in one place. No more losing track of where you applied.
Step by step
Pay depends on seniority, location and the company. Many listings on this page show the range the employer published, so compare those directly.
Analytics, ML, experimentation, forecasting? Companies often blur these in job titles. Know your strength and target roles that match it.
Not "built a classification model." Write "reduced customer churn by 12% using a gradient boosting model on 18 months of behavioral data." Lead with the outcome, then the method.
A one-sentence note explaining why you fit the specific role helps. Companies value candidates who read the job description before applying.
Profile guide
Not all profiles get responses. These are the signals that separate candidates who get interviews from those who don't.
Lead every project description with the business result. "Improved forecast accuracy by 18%" is worth more than "used ARIMA and Prophet." Hiring managers care about impact.
Many data scientists understate their SQL skills. If you write complex queries daily, say so. It's the most universally required data science skill and often the first thing tested.
Describe experiments you have designed and analyzed: sample sizing, interaction effects, statistical significance. Experimentation experience is highly valued and often rare.
Name the data stack you've worked with: Snowflake, BigQuery, dbt, Spark. Companies hire for their stack. Matching it explicitly gets you further.
Mention if you've presented findings to leadership or non-technical teams. Analytical storytelling is a key differentiator for senior data science roles.
If you have experience in a specific domain (fintech, healthcare, e-commerce), highlight it. Domain context often matters more than tool selection.
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Contract & freelance AI projects
FAQ
Everything you need to know before applying for your first data scientist role.
Create a free profile and browse the data scientist roles on this page, pre-filtered for data science and analytics positions. Describe your work in terms of business outcomes: the problem, the data environment, and the measurable result. Companies on RemoteAI search profiles directly, so a detailed, outcome-focused profile can generate inbound interest before you actively apply.
It depends on seniority, location and the company, so we do not quote a typical figure. Many listings on this page show the pay range the employer published: compare those directly, and check whether the range is base salary or includes equity.
Python and SQL are non-negotiable fundamentals. Beyond that, highlight your specific data stack (Snowflake, BigQuery, dbt, Databricks), your ML experience (scikit-learn, XGBoost, forecasting), and any experimentation work (A/B testing, causal inference). Analytical storytelling (the ability to present findings clearly to non-technical stakeholders) is a differentiator for senior roles that many candidates underestimate.
Yes, for normal use. Creating a profile, browsing jobs and applying are free, up to 20 applications a day. Credits are optional: they pay for applications beyond that limit or to put an application at the top of the list. No placement fee when you get hired, no share of your salary, no exclusive contract.
Profile quality is the biggest lever. Describe 3–4 projects with: the business problem, the data environment (sources, volume, quality), the analytical or ML approach, and the outcome. Quantify wherever possible. Apply to roles where your domain background matches (for example, a fintech data scientist applying to a fintech role), and add a short, specific application note.
On RemoteAI: data scientist roles typically involve building predictive models, running statistical experiments, and working with ML frameworks. Data analyst roles focus more on SQL, dashboards, and descriptive analytics. In practice, many companies use these titles interchangeably. Always read the actual job description to understand what the role involves day-to-day. If a "data scientist" role describes mostly BI and reporting work, it is likely a mis-titled analyst role.
Yes. RemoteAI lists full-time roles, long-term contracts, and fixed-scope projects. Data science freelance work typically involves specific deliverables: a forecasting model, an analytical audit, an A/B test design. Filter by job type to see contract and freelance options.
Yes, though junior roles are less common than mid-level and senior positions. Companies hiring junior data scientists typically want strong SQL, Python fundamentals, and a portfolio with at least one end-to-end project: a complete analysis or small ML model with documented methodology and findings. Academic projects are acceptable but real-world data (Kaggle competitions, freelance work, open datasets with a business question) are more compelling to hiring teams.
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