Kickstart your Data Science Career — Panel Discussion
Tags: Data Science, Career Advice, Conference, Technology, Careers

We discussed different topics around data science and how can early professionals get started with their careers. We touched on both technical and non-technical aspects of Data Science and tech. The discussion with each speaker introducing themselves and talking about their experiences. The following questions and quotations highlight parts of our conversation.

What does your usual day look like working in Data Science?

Abeer started “I work with different industries where they come with different business problems, and what I do is try to map how data science can help like building dashboards”.

Gabriela “We are developing tools for developers, we are working with data that is open to the world.. Our day-to-day work is creating tools that make AI more accessible to everybody and contributing to open source projects such as PyTorch, pandas, apache-spark... As a manager, I try to be with my team during code reviews”

Jatin said that his day starts with meeting with clients, working with the team on deciding the tasks and which problems to solve, and at the end of the day, they would meet the clients again to review the solutions.

What advice do you have for early professionals and those who are transitioning from academia to the industry?

Sabine said that she has been building a Data Science community over the past 3 years, “There’s not a clear definition of what a data scientist is. We try to create a career path and define the roles of data scientists, they come from different flavors and backgrounds.” Also, she said “A lot of people say they have taken the training and wonder how they can get projects they can work on and gain the experience. First, look for communities, and find mind liking people, participate in competitions like Kaggle competitions and Call for Code, find the network where you can get lots of knowledge transfers.”

Jatin explained that “ Many people dive too deep into the data science role, but not focus on the business part of the role.” encouraging people to understand both technical and business aspects of data science, and then he added, “I would suggest to build your own data as well, not just participate in Kaggle competitions.”

Gabriela talked about her perspective as a Machine Learning Manager “One thing I would like to see is a portfolio, if there’s something that you do, make sure to make it available through GitHub or write a blog about it, communicate your results. What is the goal of this project, why are you using a certain technique, how can you explain something to a non-technical audience”

What does it mean to be a woman in tech? And do you have any advice for women who would like to start their careers in tech?

Gabriela expressed how joining a company where she had allies who can advocate and support her is essential.

Abeer talked about her experience when she first joined the technical sales team in Dubai, “I was the only female in the team, but my team was very supportive, and I was mentored by my colleagues there. Also, It helps a lot when you have a leader who can support and mentor you.”

Anchal mentioned that soft skills are very important, “It is something that I have learned while working at IBM where I started working with various teams, I realized that it is something important, this is where you can actually express your skills. Technical skills can be useless if you cannot express them really well with soft skills.”

Sabine leads Women In Tech in IBM MEA which she described as “Which is currently shaping on few parameters. I spoke to a lot of women and they said they need role models in tech, speaking opportunities… Having mentors and Sponsors as allies have a big impact on the career… Build your social eminence, build your brand in terms of who you are and what you are contributing.”

It was my pleasure to talk with a group of amazing and inspiring IBMers in the world of Data Science. This blog was a highlight of our conversation. To watch the entire session and listen to what our speakers got to say visit Panel Discussion — Your Data Science Career.

You can watch more replays of all sessions in the events on NLP, AI Fairness, MLOps, Data Decision Making, and Auto ML on IBM Developer Crowdcast.

You can also view the resources for the event on GitHub.

I hope you found the article and event useful to guide and inspire you to launch your Data Science Careers! I will be ending my blog with the following quotation.