Daniel Suh Email & Phone Number

Daniel Suh is a Director of Engineering (Ranking) at Toss. He possess expertise in data analysis, r, bloomberg terminal, arcgis, microsoft office and 20 more skills. Colleagues describe him as "Daniel is an exceptional technical scientist, a great teammate, and an effective communicator. I worked alongside him for about two and a half years at Nike, and during that time he showcased an impressive knack for both managing complex technical projects and sharing his work with both technical and non-technical stakeholders. As a teammate, Daniel is someone I would regularly turn to for feedback, as he has a special talent for quickly understanding problems and offering clear, unique perspectives on how to approach them. I highly recommend Daniel; any team would be lucky to have him, and he will be missed.", "I had the privilege of working with Daniel for three months after I joined the LTV & Segments data science team at Nike. As Daniel was the senior data scientist on the team he was my go-to-person to get up-to-speed on the LTV model, which he essentially built from scratch over the last 2.5 years. Daniel had a knack for succinctly explaining both the functionality and the science behind the LTV model. I appreciated that he practiced a healthy balance of listening to understand and expressing his ideas in a thoughtful and considerate, yet persuasive way. The pythonic code that he wrote has allowed me to come up to speed quickly and become a better data scientist by being exposed to his many coding and data science best practices. Any employee would be lucky to work with Daniel.", and "Daniel is an outstanding data scientist who combines strong technical knowledge with a fundamental understanding of business issues to drive business value. On each product and model Daniel worked on, he was able to take a principled approach to planning the project, researching potential methodologies, and delivering a robust, functioning data product. His understanding of what it takes to productionize data science models drastically lowered the time from MVP to scaled product. In addition to his core data science responsibilities, Daniel showed strong aptitude and desire to work on full-stack engineer challenges including DevOps, data pipelines, and scalability. Daniel is a great team player and has mentored junior data scientists on the team. He is a very strong, natural communicator that is able to explain technical concepts to a less technical audience."

Listed skills include Data Analysis, R, Bloomberg Terminal, Arcgis, and 21 others.