The Bay Area Chapter of Women in Big Data (WiBD) was thrilled to host a panel discussion on Career transition to Data Science, Artificial Intelligence (AI), Machine Learning (ML) and Big Data and opportunities.
On October 5th, 2023, our esteemed panel of four path breaking and career pivoted women, Dimitra Tsiaousi, Elaine Chang, Emiko Sano and Rachael Rho, joined in an engaging conversation. The panel was moderated by Bay Area chapter’s core team member and Senior Research Scientist, Marilena Stavrides.
Marilena’s love of data goes back as long as she remembers. She sits at the intersection of data and business insights. She has a Ph.D. in Pure Mathematics and has been working for almost 20 years with companies helping them draw meaningful insights from their data. Marilena brings in high energy to teams and has been organizing events for Women in Big Data. She is currently a Principal Data Scientist at Acquis Consulting Group.
There are many paths to become a Data Scientist. How to follow a career in data is one of the questions we are most frequently asked. The one that Marilena gets asked even more often is ‘Are your curls natural?’
To answer the questions you have and even some of the questions you didn’t know you had we invited four very talented ladies that each followed a different path to become a data scientist. Our panelists come from across the globe, grew and studied in different cities but they have many things in common, including their love for data. Meet, in alphabetical order, Dimitra, Elaine, Emiko and Rachael.
Let’s get to know our panelists!
Dimitra was born in Thessaloniki – Greece’s prettiest city – and is a trained Civil Engineer that transitioned to Data Science while working at geo-data company Fugro. She has a Masters in Engineering and an MBA from UC Berkeley and is currently a Machine Learning Engineer at Pinterest.
Elaine is a native Californian and UC Berkeley alumni – Go Bears! Elaine is a career switcher – she has re-potted herself a few times in her career in different industries but data has been in the roots of it all! Elaine started her career in data and analytics while consulting at PwC then went on to work at an education non-for-profit. Most recently she earned her MBA and Masters in Data Science that further enabled her career change.
Currently Elaine is an Engagement Manager at Scale AI helping to build their Enterprise Generative AI practice.
Emiko is a core team member of the WiBD’s Bay Area Chapter and has been a scientist throughout her career. She worked as a lab researcher during which she found her interest in data science. She has a PhD in Microbiology from UC Davis and improved her data science skills through a bootcamp and fellowships. She is currently a Data Scientist at V2Solutions
Rachael had always been interested in Data and Statistics and has adapted to the changing industry over the years. She initially started as an Analytics consultant also at PwC and then took a risk and left to study at a Data Science bootcamp; she subsequently worked at Blue Apron combining her passion for data science and food. She completed her MSc in Machine Learning at Georgia Tech and studied Statistics at NYU for undergrad.
Now further interested in exploring the future of technology work and activism, she is currently freelancing as a Data Scientist with MoveOn.org as one of her main part-time clients.
Our panelists shared their advice –
Choose the path that fits with your life and individual circumstances
There are multiple paths available to anyone who wants to become a data scientist. The most straightforward, and most expensive, is via formal education. Studying for a masters in data science or a similar field, either online or on campus, will teach everything you need to become a data scientist. The structured recruiting process during the course will help open doors and simplify the job hunting process. However, it requires a big financial commitment; not only you will not have income for a year or two if studying full time, you will also need to pay the course fees and school and living expenses.
On the other end of the spectrum is self study. Much more economical, you can set the pace to fit with your life. It can be easy to lose momentum if you do not have a strict schedule to adhere to; study with friends or colleagues to stay motivated.
Leverage the resources available to you: bootcamps, online courses, college resources, mentors & coaches
There are multiple resources available to you: bootcamps, online training platforms, libraries, meetup self study groups. Talk to people that have traveled the path you are following and get their advice on what worked for them. Try different things and do not be afraid to adjust your approach if it is not working.
Our panelists have used Coursera, Data Camp. One of them attended Metis and enjoyed the experience. They have experience or knowledge of the analytical masters offered at UC Berkeley, Georgia Tech and recommend them – but also suggest any aspiring data scientists to review multiple course offerings from many schools, to find the best fit for them.
Studying is more fun with friends
It is easy to lose motivation or interest to study – stay engaged and enthusiastic by studying with a group of friends or classmates. If none of your friends are interested in data science, look for groups on social platforms including Facebook or Meetup. The healthy competition will keep you engaged and you are building your network of data scientists, a valuable resource in your professional life.
Network, Network, Network!
With everyone. With colleagues and classmates to develop a strong group of peers. With data scientists in the workforce, to get advice, learn from them and maybe get job leads. With recruiters so you are top of mind when they are looking to fill a position.
Practice your 30 second elevator speech, register the reaction of people you meet and refine it, to make it more effective. This is not the time to be modest. Talk about your skills and successes – be your own advocate!
Take the time to research the person and their company and be clear on what you need from them. Follow up when you say you will follow up – and don’t be late!
We are sure you are excited and maybe a bit scared to follow your heart to data science. Don’t be. It will be a challenging path but you will be glad you followed it!
The attendees enjoyed insightful discussions and asked questions. One attendee thanked the organizers and said the session was very informative and it made her see others’ DS journey as her own. One participant added that she too had inhibitions and fear of breaking into the DS, AI, ML world and that she was happy to know others’ experiences.
The Bay Area Chapter’s Co-Director, Soumyasree Vinod supported and provided valuable inputs to host the panel. Soumya introduced the Women in Big Data organization and its mission to the audience. Soumya connected WiBD’s goals of Connect Cultivate Champion with how we do it through various programs, trainings, presentations, mentoring, new hackathon initiative and providing a platform to our members to connect, collaborate, learn and grow.
Bay Area Chapter’s core team members, Marilena Stavrides and Rupa Gangatirkar arranged the panel discussion. Chapter’s core team member, Emiko Sano was instrumental in providing all backend support and help as and when needed, Thank you!
Thank you to Bay Area Chapter’s Co-Director, Erika Luncford for providing logistics support and event promotion. We thank all the core team members of the Bay Area Chapter and WiBD Executive Board member, Shala Arshi for her support.
Listen to the full panel here and join Women in Big Data to have a companion and support on this journey.