Milad Bahrami
About Milad Bahrami
Milad Bahrami is a Senior Data Scientist at Deriv in Malaysia, where he has worked since 2022. He has a strong academic background in Artificial Intelligence and Computer Science, holding a PhD from Ferdowsi University of Mashhad, and has previously held research and engineering positions in Iran.
Work at Deriv
Milad Bahrami has been employed at Deriv as a Senior Data Scientist since 2022. His role involves leveraging data science techniques to enhance business processes and decision-making. Prior to his current position, he worked as a Data Scientist at Deriv from 2019 to 2022, where he contributed to various projects aimed at improving operational efficiency and customer engagement. His experience at Deriv spans a total of four years, during which he has developed and implemented data-driven solutions.
Education and Expertise
Milad Bahrami holds multiple degrees from Ferdowsi University of Mashhad. He earned a Bachelor of Science (BSc) in Computer Science from 2009 to 2014. Following this, he pursued a Master of Science (MSc) in Artificial Intelligence from 2014 to 2016. He further advanced his studies and completed a Doctor of Philosophy (PhD) in Artificial Intelligence from 2018 to 2023. His educational background provides a strong foundation for his expertise in data science and artificial intelligence.
Background
Before joining Deriv, Milad Bahrami gained experience in academia and industry. He worked as a Scientific Researcher at Ferdowsi University of Mashhad from 2014 to 2016 and later served as an Artificial Intelligence Researcher at Shahid Beheshti University from 2017 to 2018. Additionally, he held the position of Machine Learning Engineer at Notopia for six months in 2018. His diverse background in research and practical applications of data science has equipped him with valuable skills.
Achievements
Milad Bahrami has made significant contributions to data science projects during his career. He developed a fraud detection model using Google Vertex AI, which successfully identified over 80% of known fraud cases. He also implemented a churn prediction model that led to a 20% reduction in churn rate for the marketing team. Furthermore, he improved customer satisfaction by 15% through the development of a natural language processing model that summarized and categorized customer opinions. His work has had a measurable impact on business outcomes.