Sam (Amir) Farzadfar, PhD
About Sam (Amir) Farzadfar, PhD
Sam (Amir) Farzadfar, PhD, is a data science professional with extensive experience in machine learning and analytics. He currently serves as the Manager of Data Science and Customer Implementation at Turing Labs Inc. and has held various roles in data science and engineering across multiple companies since completing his PhD in Materials Science & Engineering from McGill University.
Current Role at Turing Labs Inc.
Sam Farzadfar serves as the Manager of Data Science & Customer Implementation at Turing Labs Inc. since 2022. In this role, he oversees data science projects and ensures successful implementation for clients. His responsibilities include managing teams, developing data-driven strategies, and enhancing customer experiences through data analytics.
Professional Experience at Omdena
Since 2021, Sam has been working as a Senior Machine Learning Data Scientist at Omdena. His work focuses on machine learning applications and data science methodologies. He has contributed to various projects that leverage data to solve real-world problems, particularly in the realm of social impact.
Previous Employment at Corning Incorporated
Prior to his current roles, Sam worked at Corning Incorporated as a Senior Process Data Lead from 2016 to 2021. During his tenure, he was responsible for analyzing process data and implementing improvements to enhance operational efficiency. His experience at Corning provided him with a strong foundation in data analysis and process optimization.
Educational Background in Materials Science
Sam earned his Doctor of Philosophy (PhD) in Materials Science & Engineering from McGill University, where he studied from 2008 to 2012. He also holds a Master’s Degree (MSc) in Materials for Structures and Energy from Université Paris Sud (Paris XI), completed from 2006 to 2008. His academic background supports his expertise in engineering and data science.
Expertise in Data Science and Machine Learning
Sam possesses extensive expertise in handling imbalanced datasets, particularly for fraud detection using machine learning models. He specializes in time series analysis, forecasting, and anomaly detection. Additionally, he is skilled in advanced natural language processing techniques and is proficient in various data visualization tools, enhancing his ability to communicate complex data insights effectively.