Anna Anisin – Founder & CEO at Data Science Salon

Lauren Stern – Data Science Community Lead at Audi of America

Data Science is a relatively new term circulating around the tech world. Unlike many other of our more established partner fields such as Statistics, Computer Science, and Computer Engineering we do not have the luxury of relying on institutional degrees and countless years of direct experience to determine a candidate’s ability to perform their job functions. Data Science is in the unique position of being driven at its core, by a predominantly peer led community. Building a strong Data Science Community means more than just checking off a box. It means enabling your workforce with critical technical skills, promoting the adoption of advanced analytics, and providing overall faster customer response times. It means creating an inclusive space where your employees feel heard and are able to share best practices, current issues, and new project ideas and technologies.

Dive deeper with us into what it takes to get a community set up, where some common pitfalls occur, the expected benefits for stakeholders, and finally some tips and tricks for growing your community and helping it thrive!

Laura Edell – Chief Data Scientist, AI Markets & Innovation at Microsoft

Angela Bassa – Senior Director of the Data Science and Analytics Center of Excellence at iRobot

How do you grow and lead a high-performing data science team? As someone evaluating whether to join a new team, how do you know what to expect? What should you look for? Data scientists come from all walks of life and contribute in their own ways. Teams need to recognize that, care about each other’s work, know how to connect it to the business, and foster a diverse and resilient environment. This talk will explore how to identify, join, and build healthy data science teams. It is relevant for students and early-career professionals who are interested in exploring data science and understanding what skills and qualities are sought-after within the field. While specific to data science, this talk is also valuable for midcareer professionals and managers across a variety of disciplines who want to know how to instill a positive culture.

Smiti Sharma – Sr. Director, Systems Engineering

The volume of data, the exponential velocity with which new data is generated is talked about much. What is also becoming apparent is the business need to maintain data quality, discover insights from this data while maintaining parameters of regulation and data guardianship. In this talk, we will talk about how companies are using AI ML embedded in transactional systems that deliver real time insights to their customers. We will touch upon data governance and data sovereignty requirements that have been widely used by various industry domains

Vidhi Chugh – Staff Data Scientist at Blue Yonder

Multiple AI-driven solutions exist in the banking industry, but considering the critical role it plays in the economy and the consumer interaction, it needs more explainability on the prediction outcome. The talk intends to use Bayesian networks to better understand causal reasoning and generate insights on the credit worthiness of the borrower.

Piyanka Jain – President & CEO at Aryng

This session will lay out a methodology to identify the most promising projects for a company (i.e., the analytics roadmap). And then, we will showcase how to execute those projects with minimal resourcing rapidly. The recipe we share can serve as a playbook for any head of Data Science/Analytics to drive maximum value from their company.

Beata Kouchnir – Director of Machine Learning at Glassdoor

One of the main reasons that motivates people to get into machine learning is the incredible pace of innovation and potential for world-changing impact. While we all get very excited about the latest breakthroughs coming out of think tanks such as Google Research and OpenAI, for most of us, those discoveries have little in common with our day-to-day work. This talk addresses the less glamorous, but still very complex reality of applying machine learning to “real world” business problems.