Teresa Borcuch – Sr Data Scientist at iRobot

For hiring software engineers, coding challenges can take many forms, ranging from quick brain teasers to writing hundreds of lines of code. The goal is to determine whether or not a candidate has the technical ability to perform in the role they are applying to. I have also found coding challenges to be valuable when assessing prospective data science hires’ programming, analytical, and communication skills – some of the keys to success in a data science role. In this talk, I will describe some of the pros and cons of using coding challenges in the hiring process for data scientists, how I designed one, and how it evolved with the needs and responsibilities of the team.

Gabriela de Queiroz – Sr. Machine Learning Manager at IBM

As a Data Scientist (or aspiring Data Scientist) we are overwhelmed by the amount of knowledge we need to have and acquire. Every day there is a new technique, a new framework, a new state of the art model. For the last few years, Deep Learning has become a hot topic and it is the main driver of many applications. But how can we start our Deep Learning journey? Which of the several deep learning frameworks should we use? Where can I find examples of code that work and that I can use without worrying about the license?

In this talk, I will show you how you can start with Deep Learning without any previous Deep Learning knowledge and how you can have a basic ready-to-use deep learning “service” running in less than five minutes.

Kay Sin – Technical Program Manager at LinkedIn

Persistent self-doubt can go hand in hand with an insatiable need to prove one’s competence—or rather, to disprove one’s incompetence. Because early career experiences can influence one’s attitude towards future opportunities, it is critical to break the cyclical thoughts that can lead to an unhealthy work/life balance and a loss of sense of self. This dialogue aims to share the methods that I have employed to build stronger self-awareness, interpret situations more constructively, and restructure how I spend my time to gain confidence and support. Attendees in similarly early career stages will better understand the source of their imposter syndrome and learn the possible paths forward. Attendees in more mature career stages will be able to reflect on their past experiences to share tips and advice that they have found to be effective.

Sarah Koehler – Data Scientist at Scribd

Christina Echagarruga – Data Scientist at Scribd

Obtaining spurious results on hypothesis tests is a significant risk for analysts and business users when tools and methods are easy to accidentally or intentionally misuse. We cover two scenarios where these errors are common: multiple comparisons and sample size estimation. We will discuss the pitfalls in the currently available tools, how they affect your results, and how you can build solutions to help users avoid them.

Dhivya Rajprasad – Lead Data Scientist at Levi Strauss & Co.

Levi Strauss and Co has always been at the helm of innovation with their classic denims and seasonal takes on the future of denim . We would like to enable users who visit our website, receive our emails and visit our stores to have the most personalized experience with easier product discovery. To enable this, I have built recommendation systems based on live and past user behavior and with minimal infrastructure.

The talk would feature two main areas:

• How to work with minimal data , implicit feedback and business to build recommender systems that satisfy users needs while keeping in mind overarching business KPIs
• How to use real stream of events and past indications to give a completely personalized experience that can keep updating based on user interaction with minimal architectural requirements.

Jing Sang – Analytics Manager at Dropbox

How mindset, execution and adaptability play a critical role in achieving goals in life, professionally and personally.

Deborah Berebichez – Chief Data Scientist at Metis

Most people are unaware of a fundamental truth: everyone uses data to analyze the world. This truth is like the atmosphere: all around us, essential for life and yet invisible, so few people recognize it. A pity, because if more companies understood this truth, it would improve every aspect of their business. Deborah Berebichez, Chief Data Scientist at Metis, explains in non-technical language why it is important to create business environments where everyone contributes to and learns from the company’s data and how people and data can work together for better business.