Senior Data Scientist
As a Senior Data Scientist at Monetate I am responsible for developing new algorithms and features to support Monetate's mission to enable E-Commerce customers to create personalized experiences for the users of their storefronts. Those responsibilities had me performing the following tasks.Automated the delivery of relevant and personalized product recommendations to users for E-Commerce storefronts by developing recommendation systems based on collaborative-filtering and content-filtering algorithms.Designed and implemented a real-time data streaming application featuring a multi-armed bandit recommendation system, leveraging Amazon Web Services tools, including Kinesis for data streaming, Apache Spark for distributed processing, and EMR (Elastic MapReduce) for scaling and managing Spark.Implemented clustering techniques (KMeans, DBSCAN) for Customer Segmentation, which enabled E-Commerce vendors to identify user attribute trends (such as age, location), and align marketing efforts to those users’ interests.Conducted A/B/n tests to estimate which experiences for customer websites are more optimal in terms of E-Commerce related metrics such as Click Rate, Conversion Rate, Revenue Per Session, Bounce Rate and others.Applied advanced time series forecasting techniques, including ARIMA and Exponential Smoothing, to analyze historical sales data, resulting in accurate predictions and strategic insights that contributed to informed decision-making and improved business performance.Initiated and executed the design and prototyping of experiments to assess the viability and cost efficiency of new recommendation algorithm ideas.Visualized and rationalized the results of recommendation algorithms, data models and A/B/n Tests for internal and external customers using tools such as Looker, Streamlit and MatplotLib.
