Hafsa Dawood

Hafsa Dawood

Senior Data Scientist @ General Mills

About Hafsa Dawood

Hafsa Dawood is a Senior Data Scientist at General Mills with a background in business analytics and extensive experience in machine learning and data engineering.

Company

Hafsa Dawood currently works at General Mills as a Senior Data Scientist. At General Mills, she deploys and maintains machine learning models on Google Cloud Platform (GCP), showcasing her expertise in leveraging cloud-based technologies for data science applications.

Title

Hafsa Dawood holds the title of Senior Data Scientist. In this role, she has taken primary ownership of AutoML workflows, managing 9 modeling pipelines across 3 different metrics, reflecting her substantial experience and responsibility in the field.

Education and Expertise

Hafsa Dawood received a Master of Science (MS) in Business Analytics from the University of Southern California - Marshall School of Business in 2018. She also holds a Bachelor of Engineering (BE) in Electronics and Communication Engineering from Muffakham Jah College of Engineering & Technology, completed in 2013. Her expertise includes time series forecasting techniques, such as Naive and ARIMA models, and causal models like Random Forest and AutoML Tabular Regression.

Background

Hafsa Dawood has a diverse work history in data science and engineering. She was previously a Consultant at Deloitte from 2019 to 2021. Prior to that, she worked at Infosys in various capacities: as a Senior Systems Engineer in Hyderabad, India (2016-2017) and Melbourne, Australia (2014-2016), a Systems Engineer in Hyderabad, India in 2014, and a Systems Engineer - Trainee in Mysuru, India (2013-2014).

Achievements

Hafsa Dawood has achieved significant milestones in her career. She integrated enhanced trade allocation source features into a RandomForest model, closing a 10% gap in indirect trade account visibility. She has also taken primary responsibility for AutoML workflows, managing multiple modeling pipelines, and utilizes advanced time series forecasting techniques and causal models for forecasting.

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