Risk Data Scientist - 100% remote

🌍 Remote | Long-term opportunity | Proxify

Proxify connects talented tech professionals with long-term remote opportunities at leading companies across the USA, Sweden, Germany, the Netherlands, and beyond.

Since 2018, we’ve helped 5,000+ developers find remote opportunities with 1,200+ companies.

We believe great talent can work from anywhere — and that skills and experience matter more than location.

📍 Important: You must be located in the Americas to be considered for this position.

🚀 About the role

We’re looking for a Senior Risk Data Scientist to join a highly technical, product-focused team working on financial modeling, risk analysis, and cloud pricing.

This role is ideal for someone who combines:

• Strong Data Science and Python skills
• Experience with risk management, quantitative finance, financial engineering, or similar fields
• Hands-on Data Engineering experience
• The ability to turn complex models and ideas into real production systems

You’ll work with large-scale cloud and financial data, helping the team better understand and predict cloud usage, costs, risks, and financial outcomes across AWS and GCP.

💡 What you’ll do

• Develop and improve risk, pricing, and forecasting models
• Analyze and predict cloud usage and spending patterns across AWS and GCP
• Test different modeling approaches and improve existing models
• Build and maintain reliable data pipelines
• Work with large financial, transactional, billing, cloud usage, and infrastructure datasets
• Turn data science experiments into production-ready solutions
• Collaborate closely with Data Science, Engineering, Product, and other teams

🎯 What we’re looking for

Essential experience

• Strong professional experience in Data Science or Data Engineering
• Professional or academic background in risk management, quantitative finance, financial engineering, or a closely related quantitative field
• Advanced Python skills
• Strong knowledge of statistical modeling, forecasting, risk modeling, experimentation, and/or optimization
• Experience building quantitative models related to risk, pricing, financial outcomes, uncertainty, or time-series forecasting
• Ability to build, evaluate, troubleshoot, and improve predictive models
• Experience working with large-scale financial, transactional, cloud usage, infrastructure, or billing data
• Experience building and maintaining scalable data pipelines and data processing systems
• Experience taking models from experimentation to production
• Good understanding of data architecture, system design, and analytics infrastructure
• Strong ownership, independent problem-solving, and technical judgment

⭐ Nice to have

It’s a plus if you have experience with:

• AWS and GCP, especially cloud usage, billing, or cost data
• FinOps, cloud cost optimization, cloud economics, or infrastructure pricing
• Probabilistic modeling, financial forecasting, portfolio/risk analytics, or pricing models
• TypeScript and/or Rust
• Machine Learning workflows and MLOps
• Building customer-facing analytics or data-driven product features
• Using AI-assisted development and research tools
• Reading technical or academic research and turning useful ideas into practical solutions
• Working in small, highly collaborative teams with strong written and verbal communication

🌎 Why work remotely with Proxify?

🚀 Grow your tech career

Get access to long-term remote opportunities with interesting product companies around the world.

🎯 Find roles that match your experience

We match your profile with relevant opportunities, so you don’t have to start from scratch every time.

🏡 Work from anywhere

All Proxify roles are 100% remote, giving you the freedom to work without long commutes or being limited by your local job market.

⚡ Apply once, access multiple opportunities

You only need to go through the Proxify application process once. If you’re successful, we can recommend you for future roles that match your skills and experience.

💙 Ready to take the next step?

If you’re passionate about Data Science, quantitative modeling, risk, and building real-world solutions, we’d love to hear from you.