Daniel Chen

Daniel Chen's work experience demonstrates a diverse background in machine learning engineering, bioinformatics, scientific research, and web development.

Daniel most recently worked at Wurl as a Machine Learning Engineer, where they led the development of natural language processing machine learning for product enhancement and future product development. Daniel researched and created new machine learning models and features to improve accuracy of existing models. Daniel also streamlined existing machine learning pipelines for better reproducibility, maintainability, and cost savings.

Prior to that, at Gilead Sciences, Daniel held multiple roles including Senior Bioinformatics Associate and Biostatistician. In these roles, they built a data engineering stack using Kubernetes and Airflow, saving 50-80% in cloud costs. Daniel also developed advanced image recognition machine learning models and methodology for potential clinical trial usage. Daniel analyzed next generation sequencing data and successfully replaced contract research organizations by building internal analytical data pipelines. Daniel pioneered the development of modern data engineering approaches and company-wide data visualization efforts.

Daniel also worked at Roche as a Scientist, where they designed and executed experiments, analyzed novel proteins, and spearheaded development of cross-department data visualization efforts and protein data warehousing initiatives.

In addition, they have experience as a Freelance Web Developer and held internship positions at University of California, San Francisco (UCSF) and UC Davis. At UCSF, they analyzed RNA-Seq data and created statistical models and algorithms using microarray data. At UC Davis, they conducted research on comparative respiratory biology, functional glycobiology, and stem cell research.

Overall, Daniel Chen has a strong background in machine learning, bioinformatics, scientific research, and web development, with a focus on data analysis, data engineering, and machine learning model development.

Daniel Chen has a diverse education history. Daniel first obtained a Bachelor of Science (B.S.) degree in Biotechnology from the University of California, Davis from 2008 to 2011. Following that, they pursued further education and completed a Master's degree in Data Science from Harvard Extension School during the academic year 2020 to 2021. Currently, Daniel is enrolled in the USC Marshall School of Business, where they are pursuing a Master of Business Administration (MBA) degree. As for additional certifications, they have acquired qualifications in various subjects such as communication about culturally sensitive issues, skills for inclusive conversations, confronting bias, diversity, inclusion, and belonging, driving change, unconscious bias, design thinking, adaptability during change and uncertainty, surveys and questionnaires for UX projects, Python for data analysis, advanced R programming, the R programming environment, and multiplatform mobile app development with web technologies. Furthermore, Daniel has also achieved a certificate of accomplishment in the Japanese Language Proficiency Test N4 and N3 from The Japan Foundation.