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January 5, 2022

[Webinar] Panel Discussion: Women in Data

About the Event

According to the World Economic Forum (2021), women currently only make up 32% of workers in Data and AI worldwide. Some examples of data roles that are experiencing below gender parity include: Artificial Intelligence Specialist, Big Data Developer, DevOps Engineer, Data Scientist, Data Analyst, etc.

However, there has actually been significant job growth for data-related roles in the last few years as the amount of data and demand for people who can interpret it grows exponentially. So why is it that despite this growth, there is still a significant imbalance in gender representation?

In this event, we’ll speak with women in data to learn more about their experiences working in the field and how we can work towards bridging the gap.

Our panelists will share their thoughts on the following discussion topics and have an open Q&A session at the end:

– Pursuing a career in data
– Addressing the gender gap
– Importance of mentorship
– Empowering women in data
– Opportunities for women in data

Event Details
6:30 pm -
 8:00 pm EST/EDT
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Speakers shapes graphic
Vanessa Feng
Software Engineer, Machine Learning @ Meta
Vanessa earned her Ph.D. in Computer Science from University of Toronto, with a research focus on Natural Language Processing. Throughout her career, Vanessa accumulated extensive knowledge and industrial experience with applying Data Science, Machine Learning, and Natural Language Processing in various domains, ranging from Online Recruiting, Media, E-commerce, to Integrity. In her recent role at Meta, she is focused on leveraging advanced ML techniques to identify malicious behaviors in order to safeguard the Facebook Marketplace platform.
Michelle Miao
Data Engineer, Credit Sesame
Michelle Miao is a data engineer at Credit Sesame with 2 years of industry experience working at an AI-driven and fintech startup. Her experiences include building a time-series deep learning model, NLP model, model deployment using docker and flask, building ETL pipelines using AWS lambdas, step functions, data pipeline, etc. Her areas of expertise are working serverless, data engineering, building data pipelines.
Priya Panwar
Director- Behavioural & Marketing Analytics at GTB
Priya Panwar is the Director of Marketing Analytics at GTB. Prior to joining GTB, she worked as Senior Data Scientist at Global Infrastructure Hub and in the past with research companies – Kantar & Nielsen. She is a data professional with 14 years of experience in data science, advanced analytics, consumer insights, and digital transformation initiatives across industries. She has led data-centric go-to-market and business management strategies for global Fortune 500 organizations. In her current role, she is responsible for defining data strategies, championing opportunities, and leading the development of the data products to enable stakeholders with data-driven decisions.
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