Senior Data Scientist - Fully Remote

We’re looking for a Senior Data Scientist with the engineering depth to build and ship production ML systems at scale. You’ll design end-to-end pipelines, build real-time model-serving infrastructure, and create ML systems that directly influence product performance and revenue.

You’ll work across the full ML lifecycle—feature engineering, model development, deployment, monitoring—and translate advanced research into reliable, high-performing production code.

Key Responsibilities

• Architect and deploy ML pipelines serving sub-100ms predictions at scale
• Write production-quality Python powering millions of predictions per hour
• Build feature pipelines for large, messy datasets
• Develop real-time model-serving systems supporting multi-agent orchestration
• Own model lifecycle: research → testing → deployment → monitoring
• Design API-first ML systems with strong integration into product architecture
• Implement monitoring and validation systems to detect and auto-correct model issues

Advanced Model Work

• Develop graph-based models across large ecosystems
• Build tensor representations for multi-dimensional analysis
• Create adaptive classification systems that respond to performance shifts
• Implement causal inference frameworks to uncover true performance drivers

Requirements

• MS/PhD in Computer Science, Applied Math, Statistics, or related field
• 5+ years building production ML systems with measurable business impact
• Engineering-first mindset; strong system design skills
• Expert-level Python (scientific + engineering stack)
• Experience with model serving, A/B testing, and large-scale monitoring
• Deep learning experience with PyTorch or TensorFlow
• Strong understanding of API-first ML architectures

Essential Technical Skills

• Graph theory tools (e.g., NetworkX, Neo4j)
• Tensor operations and multilinear algebra
• Time-series forecasting
• Advanced feature engineering
• A/B testing, causal inference, statistical validation
• MLOps (CI/CD/CT, versioning, monitoring)
• Explainable and reproducible ML practices