Full TimeExternal listing

Data Scientist (Remote)

Various CompaniesData Science & AnalyticsRemote (Global)Posted September 30, 2026

About this role

About the Role

We are seeking talented Data Scientists to join innovative teams across multiple organizations hiring remotely in 2026. As a Data Scientist, you will leverage large-scale datasets to build predictive models, drive business insights, and create data-driven solutions that impact millions of users worldwide.

Key Responsibilities

  • Design, develop, and deploy machine learning models for classification, regression, forecasting, and recommendation systems
  • Collaborate with cross-functional teams (engineering, product, marketing) to define metrics, run experiments, and measure feature impact
  • Build scalable data pipelines and feature stores using modern cloud technologies (AWS, GCP, Azure)
  • Communicate complex analytical findings to non-technical stakeholders through clear visualizations and narratives
  • Stay current with ML research and apply cutting-edge techniques (LLMs, transformers, AutoML) to real-world problems

Why This Opportunity?

Remote-first culture with flexible hours, competitive compensation packages (base + equity + benefits), and access to massive proprietary datasets. Companies range from fast-growing startups to established tech giants, offering diverse domains: fintech, healthtech, e-commerce, climate tech, and more.

Our Commitment

We value diversity of thought and background. All qualified applicants receive consideration regardless of location, provided you have legal work authorization in your country of residence.

Requirements

  • Education: MS/PhD in Computer Science, Statistics, Mathematics, Physics, or related quantitative field (or equivalent experience)
  • Experience: 3+ years industry experience in data science / ML engineering roles
  • Technical Skills:
    • Expert-level Python (pandas, scikit-learn, PyTorch/TensorFlow, XGBoost/LightGBM)
    • Strong SQL and experience with data warehouses (Snowflake, BigQuery, Redshift)
    • Cloud ML platforms: SageMaker, Vertex AI, or Azure ML
    • MLOps: Docker, Kubernetes, MLflow, CI/CD for ML pipelines
    • Statistical rigor: hypothesis testing, causal inference, experimental design
  • Soft Skills: Excellent communication, ability to translate business problems into ML solutions, collaborative mindset
  • Bonus: Publications at top-tier conferences (NeurIPS, ICML, KDD), open-source contributions, experience with LLMs/RAG systems

Quick Info

Employment Type

Full Time

Location

Remote (Global)

Department

Data Science & Analytics

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