Pallavi Bharadwaj

Pallavi Bharadwaj has work experience as a Senior Data Scientist at Cognota since June 2023. Prior to that, they worked at Shopify as a Data Scientist from June 2022 to May 2023. During their time at Shopify, they collaborated on the development of Markets Pro, a profitable product that helped merchants expand globally. Pallavi also led the development of shipping recommendations for international markets and analyzed merchants' growth potential.

Before Shopify, Pallavi worked at RBC as a Data Scientist from January 2021 to June 2022. Their work at RBC focused on responsible AI and data analytics. Pallavi developed a machine learning model to recommend high net-worth customers and contributed to the development of a python library called Fairness, which ensures fairness in ML models. Pallavi also incorporated Fairness into a machine learning workflow using Airflow on AWS.

Pallavi's work experience also includes roles as a Graduate Research Assistant at Simon Fraser University, where they were a core developer of the dataprep.ai python library, and as a Data Scientist Intern at RBC, where they built and deployed a model for privileged access management.

Additionally, Pallavi has experience as a Graduate Teaching Assistant at the Faculty of Applied Sciences at Simon Fraser University, a Software Engineer at Barracuda, working on bot traffic analytics and web application firewall development, and a Software Engineer at Capgemini, where they were trained in ASP.NET and worked on the IKEA project.

Pallavi Bharadwaj completed their Bachelor's Degree in Computer Science Engineering from Visvesvaraya Technological University between 2012 and 2016. Pallavi then pursued their Master's degree in Computing Science - Big Data from Simon Fraser University from 2019 to 2020. In addition to their formal education, Pallavi has obtained two certifications. Pallavi completed "Getting Started with AWS Machine Learning" from Amazon Web Services (AWS) in September 2020. Pallavi also obtained certification in "Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization" from deeplearning.ai in April 2020.