Dan Starr

About Dan Starr

Data scientist and ML platform engineer with experience spanning academic research and startup founding, specializing in productionizing machine learning and analytics solutions across diverse domains—from astronomy to industrial sensor data, automotive, farm and customer support automation. I bridge the gap between innovative ML applications and cost-efficient cloud operations.At Aquabyte, I develop and productionize ML pipelines, lead cloud platform management, and oversee infrastructure that has reduced compute and storage costs by 60%. I also manage observability, uptime, and incident response for products and services supporting dozens of models and telemetry streams from hundreds of stereoscopic edge devices, and I have identified business processes that can be improved with ML, then developed and productionized ML models that enhance customer support and device issue identification.Before joining Aquabyte, I co-developed an ML framework for consulting projects and co-founded an enterprise ML startup later acquired by GE Digital, where I held roles spanning data science, data engineering, observability, CloudOps/DevOps.My expertise lies in building productionized ML and analytics applications with clear KPI observability. I have developed a robust framework for this purpose and continue to refine it across diverse projects.I am also deeply interested in applying ML and analytics to temporal data. My career began with real-time time-series challenges in astronomy, evolved into automotive and industrial sensor applications, and expanded into CloudOps and MLOps. Recently, I have focused on IoT sensors, aggregating data and APIs to develop models for both image and time-series datasets.

Experience

1. Sr Ml Platform Engineer

   Aquabyte

   Jul 2021 — Present · San Francisco, CA, US

   Sr. ML Platform Engineer responsible for developing and productionizing ML pipelines. Lead and manage cloud platforms and infrastructure; reduced cloud compute and storage costs by ~60%. Lead observability, uptime & incident management for infrastructure, services, products which span dozens of productionized models and telemetrics coming from hundreds of stereoscopic edge devices. Conceived and developed ML models to improve customer support processes.

Education

• University of Washington

  B.S., Physics, B.S., Astronomy

  1996 — 2001

Skills

• Machine Learning
• Scientific Computing
• Mathematical Modeling
• Graph Databases
• Docker
• Terraform
• Statistics
• Computer Science
• Hadoop
• Mysql
• Simulations
• Time Series Analysis
• Spark
• Software Development
• Python
• Latex
• Astronomy
• Unix Shell Scripting
• Physics
• Data Science
• Data Analysis
• Nlp
• C
• Programming
• Optimization
• R
• Software Engineering
• Algorithms
• Start-Ups
• Xml
• Amazon Web Services (Aws)
• High Performance Computing