ELEVATING WOMEN IN DATA

THURSDAY, AUG 27 @ 1:00 PM – 4:30 PM EDT

Register Apply to speak

DATA SCIENCE SALON ELEVATE IS A UNIQUE FEMALE FOCUSED VIRTUAL CONFERENCE WHICH BRINGS TOGETHER SPECIALISTS TO EDUCATE EACH OTHER, ILLUMINATE BEST PRACTICES AND INNOVATE NEW SOLUTIONS. LEARN AS OTHER FEMALE THOUGHT LEADERS SHARE THEIR WISDOM AND EXPERIENCE IN THEIR CHOSEN FIELD.

Speakers Include

Amy Gershkoff Bolles

Chief Data Officer at BitlyIQ

Anupama Joshi

Dir. AI, Personalization, Computer Vision & MLaaS at Target

Rhonda Textor

Head of Data Science at True Fit

Sivan Aldor-Noiman

Head of Data Science & Data Engineering at Wellio

Nandika Donthi

Data Engineer - Active Learning at Wellio

Nicole Hu

CTO at One Concern, Inc

sample of topics covered:

Machine learning best practices
Data strategy and governance
Data ethics and bias
Recommendation engines
Designing ML pipelines efficiently
Career paths
Hiring and Recruitment
Seeking mentorship with purpose
The importance of female-first spaces
Navigating male-dominated teams
Overcoming imposter syndrome
Data science teams: managing, building, collaboration
Integrating open source tools into your workflows
Data and AI for emerging platforms
Data governance

About DSSe

DSSe is an initiative to elevate and connect the voices of women in data science and encourage companies to set a better habit to generally consider women for data-intense roles.

DSSe is important because the field of Data Science is still young — for it to fulfill its true potential, we need voices that reflect the full experience of the world we live in to contextualize our data-derived insights.

By elevating women and other underrepresented groups, we have the opportunity to build a truly incredible and thriving field that influences decision-making at every level in a meaningful way.

Schedule

(All times are US Eastern Time)

1:00pm - 1:15pm

Introduction

1:15pm - 1:45pm

Leaps and bounds with data products

Sivan Aldor-Noiman – Head of Data Science and Data Engineering at Wellio
Nandika Donthi – Data Engineer – Active Learning at Wellio

Machine learning based data product life cycles do not necessarily follow the same development paths as most conventional products. These data products bear inherent risks and rewards that are sometimes hard to quantify and even recognize in advance. These considerations present unique challenges in product management and development practices when it comes to deployment.

1:50pm - 2:20pm

Hiring and getting hired in the age of COVID

Amy Gershkoff Bolles – Chief Data Officer at BitlyIQ

In this talk we will walk through one of our machine learning data product development cycles. We will share our learnings on developing, deploying and monitoring data products in the wild, including when it’s worth taking the leap and facing the bounds that come with it.

2:35pm - 3:05pm

Advancing Career as women in Technology

Anupama Joshi – Director, AI – Pesonalization, Computer Vision and MLaaS at Target

We are almost past Two decades into 21st century but technology is still a male dominated field. Progressing in tech career as women is still not easy. There are some issues about the culture in tech industry and there is something we as women can do, individually and collectively. In this talk I will talk about my experiences and what I learnt from experiences of my collogues, friends and other women leaders, which helped me to make progress in my career. Hope this will help you too.

3:10pm - 3:40pm

Augmenting Existing Physical Models with AI to Create Impactful Disaster Resilience Solutions

Nicole Hu – CTO at One Concern, Inc

The best products are designed around improving real-world outcomes. In a discussion about using AI to create impactful solutions, Nicole Hu details the importance of approaching problems with an open mind – using the technology, or combination of technologies, that is most effective for solving a particular problem. In an era filled with countless AI and machine learning solutions, she argues that AI is not always the answer.