Data Scientist
WarppoolAbout the role
Company Description Warppool provides massively parallel compute power for workloads such as Monte Carlo simulations, ML inference, and rendering by utilizing billions of idle GPUs in phones, laptops, and desktops through web browsers. By shifting computation to existing consumer devices, Warppool eliminates the need for new silicon, substations, or cooling infrastructure. This approach reduces reliance on traditional data centers while enabling scalable, high-performance computing. Team members collaborate on cutting-edge distributed systems and browser-based technologies to help redefine how compute resources are accessed and used.
Role Description The Data Scientist will work in a remote, contract capacity, focusing on analyzing large-scale parallel computing data generated from browser-based GPU workloads. Day-to-day responsibilities include designing and implementing statistical models, conducting exploratory and predictive data analysis, and building data visualizations to inform product and infrastructure decisions. The role involves collaborating with engineering and product teams to define metrics, evaluate performance of workloads, and optimize resource utilization. The Data Scientist will also help develop analytical frameworks, document findings, and communicate insights to both technical and non-technical stakeholders.
Qualifications
Candidates should possess strong skills in Data Science for modeling, experimentation, and insight generation.
Candidates should possess skills in Statistics and Data Analytics to design robust analyses, interpret results, and support decision-making.
Candidates should possess capabilities in Data Visualization to create clear dashboards, charts, and reports for diverse audiences.
Candidates should have experience with demand forecasting and time-series modeling.
Proficiency in Python or R, including common data and ML libraries (e.g., pandas, NumPy, scikit-learn, or equivalent).
Experience working with large or distributed datasets and modern data tools (e.g., SQL, Spark, or cloud-based data platforms).
Ability to design experiments, define metrics, and evaluate performance of algorithms and systems.
Be comfortable using AI tools like Claude Code and Paper.
Know GitHub, Git and CI/CD tools like GitHub Actions.
Strong communication skills and ability to collaborate effectively in a fully remote environment.
Bachelor’s or advanced degree in a quantitative field such as Data Science, Statistics, Mathematics, Engineering, or a related discipline, or equivalent practical experience.
Responsibilities
- Design and implement statistical models
- Conduct exploratory and predictive data analysis
- Build data visualizations to inform product and infrastructure decisions
- Collaborate with engineering and product teams to define metrics
- Evaluate performance of workloads and optimize resource utilization
- Develop analytical frameworks and document findings
- Communicate insights to technical and non-technical stakeholders
Qualifications
- Strong skills in Data Science for modeling, experimentation, and insight generation
- Skills in Statistics and Data Analytics for robust analyses
- Capabilities in Data Visualization for clear dashboards and reports
- Experience with demand forecasting and time-series modeling
- Proficiency in Python or R and common data and ML libraries
- Experience with large or distributed datasets and modern data tools
- Ability to design experiments and evaluate performance of algorithms
- Comfortable using AI tools and CI/CD tools like GitHub Actions
Skills mentioned
About Warppool
Warppool runs embarrassingly parallel workloads—Monte Carlo, ML inference, rendering—by harnessing billions of idle GPUs in phones, laptops and desktops via browsers. No new silicon, substations, or cooling towers: move compute to existing devices and avoid building more data centers.