Whether you already work in data science/machine learning or plan on getting into the field, you realize that there is no one way for this career path. For this event, we have invited speakers from different companies and tech communities to talk about their personal journey into data science and beyond and share some lessons they've learned along the way.
6:00 Doors open
6:30 Welcome from WiMLDS, PyLadies SF, DoorDash
6:40 Talks by Melanie Warrick, Kasia Rachuta, Raquel Araujo
8:00 Panel discussion with the speakers and invited guests, socializing
9:00 Doors close
Speakers and invited panelists:
Melanie Warrick is a Developer Relations Engineer for AI and the Cloud at Google and speaks a lot about AI and ML. She was a founding engineer on a deep learning platform prior to Google and worked on machine learning engineering at Change.org. In another life she had a comprehensive consulting career, as an IC and leading teams both domestic and international and before that she was working behind the scenes in the film industry. In her spare time, she sleeps.
Kasia Rachuta works as a Data Scientist at Medium. She has a Master’s in theoretical physics from University College London. In her spare time, she enjoys volunteering for women-related organizations and diversity causes, scuba diving and traveling.
Raquel Araujo works at Indeed as a Product Scientist in the Applicant Quality team after working as a Business Intelligence Analyst. After trying to be an Economics PhD student, she found a job in analytics and never looked back.
Jessica Lachs is the Head of Analytics at DoorDash. Jessica and the team are responsible for all things data including product analytics, business analytics, business intelligence, and data science. Prior to DoorDash, Jessica was the founder and CEO of GiftSimple, a social gifting startup. She began her career in investment banking, spending over 3 years at Lehman Brother in NYC. Jessica holds a BS from Cornell, and an MBA from The Wharton School at the University of Pennsylvania.
Grishma Jena is a Cognitive Software Engineer with Data Science for Marketing at IBM Watson. She volunteers at the San Francisco Public Library and has mentored high school students on Artificial Intelligence as part of the non-profit AI4ALL.
Paula Alves is a Data Scientist with Digisight, where she works with healthcare data in the production team. She learned Python for fun and turned a hobby into a career, starting with consulting. Previously, she was a geoscientist specialized in mineral processing research and development.
Talk 1: Tech career path advice (Melanie Warrick)
I've worked in many companies and industries and my path into technology has not been a straight line. I am sharing what I've experienced developing my career and advice that has helped me get where I am now. The goal is to get you thinking about your career paths in ways you haven't considered.
Talk 2: From physics to data science - a numbers journey (Kasia Rachuta)
Prior to starting my career in tech, I worked for the UK equivalent of the National Science Foundation; completed a couple of internships in social impact measurement and volunteered in Spain, Kenya and London. After I finished my master's, I decided to try something new and that's how I found myself in data science. I will cover being self-taught, what resources have helped me get where I am, as well as touch on the importance of mentorship and advocating for yourself.
Talk 3: What I learned from every job I held and how that shaped who I am today (Raquel Araujo)
My journey into data science was not a typical one. But whose is it, anyway? When I was looking for a job in a tech company, I had to market myself as ready for the industry after spending several years in academia. I had to learn to navigate whiteboard interviews, homework assignments, coding challenges. I want to share what I learned from every step in my career with people who may find themselves in a similar situation.