Matthew Dinh is a Senior Data Scientist at Fannie Mae, where he leverages his extensive expertise in advanced analytics and modeling to drive data-driven decision-making within the organization. With a strong foundation in recruitment data, survey analytics, and diversity metrics, Matthew plays a pivotal role...
Matthew Dinh is a Senior Data Scientist at Fannie Mae, where he leverages his extensive expertise in advanced analytics and modeling to drive data-driven decision-making within the organization. With a strong foundation in recruitment data, survey analytics, and diversity metrics, Matthew plays a pivotal role in enhancing Fannie Mae's talent acquisition strategies and workforce planning initiatives. His team focuses on three core areas: Analytics/Modeling, Data Infrastructure, and Research, ensuring that the organization is equipped with robust data solutions to meet its evolving needs.
Matthew's proficiency in statistical inference and analysis, particularly in cluster and regression modeling, allows him to extract meaningful insights from complex datasets. He is adept at utilizing tools such as R, Python, and SQL, alongside platforms like Amazon Redshift and Snowflake, to develop predictive models that inform strategic workforce decisions. His commitment to relational analytics and research empowers Fannie Mae to better understand its workforce dynamics and optimize talent management processes.
A strong advocate for the transformative power of data, Matthew believes that effective analytics can significantly enhance the capabilities of talent departments. By harnessing data-driven insights, he aims to foster a more inclusive and efficient workplace. His passion for data science is matched by his dedication to continuous learning and innovation, making him a valuable asset to Fannie Mae as it navigates the complexities of the modern workforce landscape.
