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Co-founder, Chief Data Scientist & Johns Hopkins University Alum Creates Data Science Startup
Salynt's Co-Founder and Chief Data Scientist, Ruthe Huang, is paving the way for women in tech.
Salynt is on the verge of releasing one of the hottest low code solutions in AI, all with the help of its Co-Founder, Ruthe Huang.
Ruthe's expertise and leadership as a Data Scientist are formidable, providing the perfect strategic advantage in AI and Machine Learning. Her passion is to provide solutions to the pain points of other Data Scientists.
She says, "Companies usually don't provide the right tools for a data scientist to succeed; they don't necessarily understand that we want to code. Taking away our ability to do that reduces our motivation to do a great job."
Ruthe's background as a data scientist began in the public sector, where she focused on biostatistics and public health. Since then, she's worked at Booz Allen Hamilton and Practical Intelligence as a Senior Data Scientist. Her time with each organization has saved the DoD millions of dollars by building applications that analyze captured KPIs.
Additionally, Ruthe has worked with MIT's Lincoln Lab and the Naval Postgraduate School to provide applications for government mission support. Her ability to combine her experiences from the world of consulting, marketing, health research, and finance has made her one of the most sought Founders on the East Coast.
Educationally, Ruthe received her master's degree in Biostatistics from Johns Hopkins University, where she also received her bachelor's degree in Public Health Studies and Applied Mathematics.
Furthermore, Ruthe teaches mathematics and statistics in her free time, mentors criminal justice-involved scholars, and enjoys hiking, water sports, and the symphony. Her commitment to the community and social impact issues is a key value add for Salynt.
To learn more about what Ruthe is building, sign up today to demo Salynt's application.
About Salynt
Salynt's platform will prepare developers for current and future workplace challenges for implementing AI initiatives. Data Scientists will be able to rapidly produce automated analytics, which gives back time to Software Engineers to focus on more complex system operation tasks. Software Engineers can develop their machine learning engineering skills, and Data Scientists can learn to roll out ML models fit for production that benefit their company and their users.