Summary
Daniel Reiff is a machine learning and field engineer with six years of experience building production ML systems for safety-critical domains, currently applying computer vision and cloud skills at Roboflow after helping develop Construction's Autonomous Monitoring System at Forsight. He blends practical software engineering (Python, Spark, TensorFlow) with cloud-native deployment on AWS to deliver real-time camera-based risk detection and predictive analytics for construction sites. His background spans data science, systems engineering and even investment banking analysis, giving him a rare mix of domain rigor, quantitative modeling, and product-minded delivery. An active writer for Towards Data Science, he communicates complex ML and visualization techniques clearly—demonstrating both technical depth and an ability to teach. Comfortable across the stack, he pairs tooling like NumPy, Pandas, Scikit-Learn, Beautiful Soup and Plotly with hands-on field experience installing and validating sensor systems. Based in New York, he focuses on scalable, robust ML solutions that drive measurable safety and operational outcomes.
7 years of coding experience
5 years of employment as a software developer
Dwight-Englewood School
Certificate Data Science, Certificate Data Science at Galvanize
Majors in Systems Engineering and Political Science Minor in Computer Science, Majors in Systems Engineering and Political Science Minor in Computer Science at Washington University in St. Louis
London School of Economics and Political Science