What we're about

This is a group for women and non-binary persons interested in Machine Learning and Data Science. We meet to socialize, and to discuss machine learning and data science in an informal setting with the purpose of building a community around women in these fields. We are openly inclusive of anyone who identifies as female, genderqueer or non-binary. Men who support our mission are invited to attend our meetups as guests of female members or with permission from the organizers (please send a message to introduce yourself!), though priority will be given to female members if an event is overbooked.

Our Code of Conduct (https://github.com/WiMLDS/starter-kit/wiki/Code-of-conduct) applies to all our spaces, both online and off.

• The official twitter account for the Bay Area meetup is @WiMLDS_BayArea (https://twitter.com/wimlds_bayarea) and our email is bayarea@wimlds.org.

• Follow @wimlds (https://twitter.com/wimlds) on Twitter for general WiMLDS news or visit http://wimlds.org to learn about our chapters in other cities.

• Women & non-binary folks are invited to join the global WiMLDS Slack group by sending an email to slack@wimlds.org.

Upcoming events (1)

WiMLDS x Harnham: Career Transitions (Virtual Talks + Networking)

Please join us for our third virtual meetup where women in leadership positions share their knowledge and experience transitioning into different roles in their career. We will have a Q&A session with the speakers and some networking time, feel free to come prepared with questions and brainstorm with others on how best to go about career advancements. Everyone supporting our mission, regardless of gender, is invited to attend. Please keep in mind our Code of Conduct applies to all meetups: https://github.com/WiMLDS/starter-kit/wiki/Code-of-conduct SCHEDULE 6:00 pm- WiMLDS Welcome and Introductions. 6:15 pm - 1st talk: "Core Competencies to transition to a Career in Data Science" , Siobhán McNamara The Data Scientist is still a relatively new role and it can mean different things for different organisations. People come from a very wide range of backgrounds into data science and despite the disparity of the role at organisations the fundamentals are consistent across all. For this presentation we will explore what those fundamentals are and how to focus on improving those in order to make the career jump. 6:40 pm - 2nd talk: "Working as a Data Scientist in Healthcare" , Nandita Damaraju Data science is being increasingly leveraged to solve many problems in the biotech industry. Within the Biotech industry, however, data science can mean many different things depending on the company or application. As a Machine Learning Scientist in the biotech industry, I want to talk about my experience navigating the plethora of opportunities available at this exciting interdisciplinary intersection, how data science in biotech differs from other industries and the rich potential of avenues combining life and data science. 7:05 pm - 3rd talk: "Transition from academia into industry data science", Sarah Wohlman, PhD Speaking about the transition from academia into industry data science, as well as the challenges that come with starting a family and juggling a job in a male-centric environment. 7:30 pm onwards - Q/A with speakers and networking session. Speaker Bios: Siobhán McNamara Data scientist, Cyber security at Agari. Originally I studied Psychology & Economics and was always interested in the intersection of the two. Various work throughout my career has been focused on analyzing risk related decisions under uncertainty. That experience has lent itself to my current work, online actors are anonymous, their true identity and motivations are easily masked. At Agari we work to unmask those and to identify when behavior is consistent versus when there are higher levels of uncertainty introduced by anomalies. Nandita Damaraju Machine Learning Scientist at Inflammatix Inc, improving diagnostic outcomes for infectious diseases. Prior to that, I worked as a Data Scientist at Thermo Fisher Scientific to make genetic sequencing instruments more efficient to use. I have a master's in Computational Science and Engineering from Georgia Tech and a Bachelor's in Biotechnology from the Indian Institute of Technology, Madras. I am deeply excited by the prospect of applying novel data science methods to problems in the biotech industry. Sarah Wohlman, PhD Sr Data Scientist, Aetna In 2009, I graduated from Bucknell University, a small teaching university, with a degree in Biomedical Engineering and a plan to attend Northwestern University for my doctorate without much idea about a research-driven academic pursuit. Six years later I emerged with my degree and a firm belief that my path forward was an industry position where I could apply the incredible soft skills required to get a PhD in a position where I could see the outcome of my work. This position was as a data scientist where I got to use the perfect mix of my skills of problem solving and project management with data analysis and coding. * Zoom Waiting Room enabled for troll control. Wait time may stretch to a few minutes while a host lets you enter, thank you for your patience! *

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