Madhu Ramiah
About Madhu Ramiah
Madhu Ramiah is the Director of Data Science at Brigit, where he has worked since 2024. He has a strong background in data science and software development, with previous roles at Dave - Banking For Humans and EMC, and holds a Master’s Degree in Computer Science from the University of Southern California.
Current Role at Brigit
Madhu Ramiah serves as the Director of Data Science at Brigit, a position he has held since 2024. In this role, he leads data-driven initiatives and oversees the development of data science strategies. His responsibilities include managing a team of data scientists and ensuring the implementation of effective machine learning solutions to enhance the company's offerings.
Previous Experience at Dave - Banking For Humans
Madhu Ramiah worked at Dave - Banking For Humans in various capacities. He began as a Senior Data Scientist from 2019 to 2020, where he contributed to data analysis and machine learning projects. He then advanced to the role of Lead Data Scientist from 2020 to 2022, focusing on developing predictive models and data-driven solutions to improve customer experiences.
Educational Background
Madhu Ramiah holds a Bachelor of Technology (B.Tech.) in Information Technology from Anna University, where he studied from 2008 to 2012. He later pursued a Master’s Degree in Computer Science at the University of Southern California from 2015 to 2016. His educational background provides a strong foundation in technology and data science.
Technical Skills and Expertise
Madhu Ramiah possesses a diverse skill set in programming languages and technologies, including Java, Python, HTML, CSS, and JavaScript. He is proficient in database management systems such as MySQL and Oracle, as well as big data technologies like Hadoop and MapR. His expertise extends to both supervised and unsupervised machine learning algorithms, enabling him to tackle complex data problems.
Projects and Machine Learning Solutions
Throughout his career, Madhu Ramiah has developed solutions for various machine learning challenges. His projects include churn predictions, demographic predictions, stock market analysis, and crime prediction. He also built a recommendation engine utilizing Natural Language Processing (NLP), showcasing his ability to apply advanced data science techniques to real-world problems.