Success stories from landing jobs in data science and software engineering at Google, Microsoft, NVIDIA, and more.
It always seems impossible until it’s done— Nelson Mandela
And another quote on the importance of real-world experience:
Nothing ever becomes real till it is experienced — John Keats
This article is not a compilation of quotes but a practical how-to guide with personal stories of people who landed their desired jobs. In the following, I want to share five inspiring journeys, from which I am sure you can take some insights to apply to your own career.
Getting a software engineering role at Google
Samir Sheriff shares his journey from several years in the corporate world to joining real-world AI projects, and finally securing a software engineering role at Google.
Samir´s key lesson:
“No course can replace the real world”
Joining several real world AI projects made me realize that machine-learning problems are not as well-structured as all the assignments and course/hackathon projects I was so used to, especially because data is complex and usually never available in the relevant form. There are so many unknown variables to consider and so many trade-offs to make in order to come up with a practical solution. Diving into these projects helped me signifcantly to improve skills that I now need to demonstrate on the job.
Read the full article here.
Switching into a data science career
Rosana de Oliveira Gomes is an inspiring Astrophysicist who is now after joining more than five AI challenges where she worked with more than 200 engineers from 30+ countries in total, a Lead Machine Learning Engineer at Omdena.
Rosana improved her leadership and “communication to non-scientist” skills and increased her multicultural experience by working with collaborators from three continents for the first time.
Rosana´s key lesson:
“Mindful communication with peers is key”
“I feel more connected with the world after speaking every day with people from all over the world in order to build something together. I also feel more confident and valued (before the experience at Omdena I have suffered from bullying at the workplace and this really helped me to rebuild my confidence in my skills).”
Read more about Rosana´s journey here.
Interning at NVIDIA & overcoming barriers
Kennedy K. Wangari from Kenya shares his learnings on balancing courses and real-world projects, mindset, and securing an NVIDIA Internship.
Kennedy´s key lesson:
“The self-trust mindset: you’ve got to trust yourself, be courageous enough to follow your passion aggressively, and believe in your capabilities.”
“Make that real progress in ML, don’t be swayed by exciting ideas, projects springing up daily, and by latest development trends. Stay motivated, focused, and build up skills. When it comes to Imposter Syndrome, the voice in your head can get very loud about how big a fraud you are and how little you know. It will focus on your shortcomings, ignoring your success. Shift it by getting louder about your achievements. For every new skill, you gain, celebrate in grand.”
Always remember this quote by Albert Einstein:
“The moment you stop learning, you start dying”.
Read more about Kennedy in the interview here.
From Omdena to a full-time offer at Microsoft
Kritika Rupauliha is a CS undergrad, currently in the 6th semester of her degree.
Kritika´s key lesson:
“Thriving happens in a diverse community of like-minded people.”
“Before joining Omdena, I had been involved in some research work and college projects under my professors. But I had never been exposed to such a big community of similar-minded individuals. I found out that I thrived in such a community, learning with my peers, and exploring the horizons of AI. Omdena is also one of the key reason why I got selected for a software engineering role at Microsoft.”
Building a real-world project portfolio
Animesh Seemendra got a Research Engineer role at SHL Global AI Team of Aspiring Minds.
Animesh´s key lesson:
“Run things from scratch and help people to ignite your creativity.”
“The project I was involved in was for social good. Helping people was a huge driving force. Doing things from scratch was so powerful. You need to build up your portfolio, which gives you experience, trains you to collaborate, and also teaches you on applying data science to real-world problem, which lacks a lot when you randomly practice from various sites.”