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.
Data Scientist at Scribd
Data Scientist at Scribd
Lead Data Scientist at Levi Strauss & Co.
Gabriela de Queiroz
Sr. Machine Learning Manager at IBM
Technical Program Manager at LinkedIn
Sr Data Scientist at iRobot
Analytics Manager at Dropbox
sample of topics covered:
Machine learning best practices
Data strategy and governance
Data ethics and bias
Designing ML pipelines efficiently
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
12:30pm - 12:40pm
12:40pm - 1:10pm
Beyond “Fizz-Buzz”: Coding Challenges for Hiring Data Scientists
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.
1:15pm - 1:45pm
Deep Learning for Everyone
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.
1:50pm - 2:20pm
Don’t Fake it, Own it: Facing Early Career Imposter Syndrome
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.
2:35pm - 3:05pm
Hacking p-Hacking: Avoiding Spurious Analyses with Automation
Sarah Koehler – Data Scientist at Scribd
Christina Echagarruga – Data Scientist at Scribd
3:10pm - 3:40pm
Real time, contextual and personalized recommendations
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.
3:45pm - 4:05pm
How to Thrive Regardless of Background and Limited Resources
Jing Sang – Analytics Manager at Dropbox
4:15pm - 4:30pm
DSS Elevate is a non-profit event. Your ticket covers our production fees so we can continue to elevate women and other underrepresented groups.