AI Insights

Inspiring Omdena Collaborator Stories Who Got Hired at Top Companies

December 25, 2021

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Success stories from landing jobs in data science, machine learning, and software engineering at Google, Microsoft, NVIDIA, and more. Also, check out the data science roadmap at the end of this article.

“It always seems impossible until it’s done” — Nelson Mandela

Data indicates the data science market is getting flooded with too many “data scientists”. There are dozens to hundreds of applications for positions, and in that sense, competition is very high.

But in another sense, there are many job openings. This disconnect is because while many people call themselves data scientists, not many people are capable of meeting the demands of most jobs in the profession. It requires a lot of difficult-to-come-by training, technical and soft skills, which is not something you can pick up with a few months of courses or a one-year master’s program.

The lack of “Real-World Data Scientists” is one of the reasons we started Omdena. In the following, I share several success stories from Collaborators who completed several Omdena AI Challenges and then secured jobs at various companies.

I am sure you´ll be inspired in your journey by reading the magnitude of perspectives and learnings from these amazing changemakers.

Omdena Job Success Stories 

From Junior Machine Learning Engineer to Computer Vision Researcher

Mahzad Khoshlessan

Mahzad Khoshlessan

What a growth journey! 24 months ago Mahzad Khoshlessan started as a Junior Data Scientist and now works as a Research Scientist on 3D Computer Vision & Robot Perception at the Stevens Institute for Artificial Intelligence. 

Mahzad went from joining her first Omdena #AI Challenge to speaking at DeepLearning.AI in front of more than 1000 people, to becoming an AI Product Owner, and now considering entering an entrepreneurship program.

From academia to data analyst, to data scientist 

Rubens Carvalho

Rubens Carvalho

Awesome growth journey by Rubens Carvalho from Brazil (also nice text on the T-Shirt ????):

“’I used to work as a researcher and teacher, but then I decided to start a journey, from physics to data science. I needed some practice. I’ve got this by being part of a multicultural team with different backgrounds, working together to solve real-world problems. That experience was crucial to my career! After that, I got a job as a data analyst for a bank and finally reached my objective: become a data scientist.

Now I work at A3Data, a company that shares the same culture as Omdena, empowering people through data!”

Becoming a Software Engineer at Microsoft: A Journey of Growth

How to Become a Software Engineer at Microsoft: A Journey of Growth

Brian Kariuki

On the value of failure

I can’t really pin down one big failure, there have been quite a number of setbacks I have had in my career that leave you questioning whether you are good enough?

Learn to accept setbacks as part of the process

Accept it and just move on. There are things that you can’t always control as there are always external factors involved. And you will never be the master of external factors. This is a great illusion.

If things are meant to go sideways they will always go sideways. The good thing is you will learn a lot from the sideways as the most important lessons are often hidden in going “off-track”. It makes you reflect on yourself and the situation you have been in. So the question you can ask yourself is, What can I learn from it? And then you move on to the next endeavor. One step back, and two steps forward.

Getting a software engineering role at Google

Samir Sheriff

Samir Sheriff

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 significantly to improve skills that I now need to demonstrate on the job.

Getting a Data Science Job Offer at Accenture 

Apoorv Mehta

Apoorv Mehta

On the value of communication skills

While you appear for a data science interview, apart from revising basic ML concepts such as Classification, Regression, evaluation methods, sampling methods, etc., it is also important to be able to explain the kind of data science projects you have done in the past. Having applied hardcore concepts in a project and not being able to explain it to the recruiter makes little sense. In this way, you need both the communication skills and real-world experience under your belt.

#Key tip: Improve your communication skills early on

Think about how to communicate your results to a non-expert or non-technical person. What problems are you solving? What impact is your solution making? How does it improve a process/ a person’s life etc.?

From Omdena to a full-time offer at Microsoft

Kritika Rupauliha

Kritika Rupauliha

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 reasons why I got selected for a software engineering role at Microsoft.” 

Interning at NVIDIA & overcoming barriers


Kennedy K. Wangari

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.”

Follow us on LinkedIn to get notified about all inspiring and insightful growth journeys. 

Bonus: Skills and experiences needed to get a job in data science

Soft Skills

The people skills.

Source: Omdena Project

Source: Omdena Project

According to WEF, two of 2025´s most important skills are analytical thinking as well as complex problem-solving. Let us apply this to the data science field and describe the most essential skills.

Complex Problem-Solving

Here are two myths about how data scientists solve problems: one is that the problem naturally exists, hence the challenge for a data scientist is to use an algorithm and put it into production. Another myth considers data scientists always try leveraging the most advanced algorithms, the fancier model equals a better solution.

The reality is each problem is unique and comes with different parameters. The essential skill is to figure out the most effective and often efficient approach to solve the problem. Sometimes it needs a fancier model but more often a simplistic approach yields better results. The skill is that you deeply analyze the problem, understand it, and then decide what solution can be built. The problem first, technology second!

Critical Thinking & Analysis

The times of heavy top-down management are (mostly) over. While competition has a more top-down style process to follow, building a real-world project works best in a collaborative approach with a flat hierarchy.

The best data scientists do not just follow orders but learn how to think independently. This will not only help to address a problem differently but also will improve team communication, educate the business leaders, and the overall leadership of an organization. All of which ties into the following skill-set:

Communication & Collaboration

A data scientist has to be able to communicate results and automate analyses. While from a technical standpoint, this is typically done in Power BI, Tableau, or similar, direct team communication is key. This means to:

  • Build empathy and cultural awareness
  • Understand how to ask for help the right way
  • Split the work amongst each other most effectively
  • And much more where competitions won´t help you.

“Communicate unto the other person that which you would want him to communicate unto you if your positions were reversed.” — Plato

Active Learning & Learning Strategies

A no-brainer. Competitions lack the collaboration and interaction necessary to learn the most, either by teaching or listening to somebody else.

Learning Pyramid

Learning Pyramid

Leadership & Social Influence

Individuals who are able to take responsibility and drive initiatives forward in a team are what every organization is looking for. Taking over responsibility is like a muscle that can be trained. You do not always need to be a senior to take on leadership roles. Not the project size matters but your mindset of moving things forward wherever you can.

You don’t need a title to be a leader.

“The strength of the team is each individual member. The strength of each member is the team.” — Phil Jackson



Lastly, fun is an essential “soft skill” to have. As AI influencer Eriber Weber puts it:

“Do not only optimize for income but for work that makes you happy.”

Happiness is the ultimate productivity driver but apart from that, work (life) is too short to do most stuff you do not enjoy or that does not serve a bigger purpose.

Technical Skills

Now, after touching on the key soft skills, I want to briefly talk about some overlooked hard skills. Apart from programming, ML, and EDA, there are some less obvious skills that make or break it.

Data engineering

Coming back to our example earlier where a senior data scientist and Redditor describes his work, most of it covered data engineering.

As a data scientist, you may join thinking you’re there to build smart models and derive as much value from the data as possible. In reality, most of the time you get held up as your first few months require you to build the necessary infrastructure and pipelines to even get the data. Having looked into some messy datasets will help you to kick-start your career.

Visualization & Analytics



Almost always visualization is ignored by beginners and even more experienced data scientists.

Here is why visualization is so important:

It can provide you some great help in:

  • Interpreting data better and memorable.
  • Getting your insights across (non-technical) folks
  • Noticing correlations
  • Figuring outliers
  • Finding Cause-Effect relations
  • And more you won’t see till you visualize it ????

Version Control

Is the ability to manage the change and configuration of an application. It’s a priceless skill in a team of developers. It allows you to check files for modifications. Next, during check-ins, you see if the files have been changed by another user and you will be alerted and able to merge them.

Paying attention to version control will make the teamwork much more effective.

API’s and Command Line

You just can’t skip it — if you do, you are bound to hear it again. APIs are being used almost everywhere and are needed to excel in developing applications in any of the data science domains such as cloud, IoT, and web applications. Having a good understanding of different storage services, security features, and automation tools will enable you to apply the best technology needed for the job.


Your model is not a Jupyter notebook!

The deployment on edge and/or cloud is a must-skill in all production applications. As a fact, to maintain a model on production with security, and maintenance is one of the rare and wanted skills in the field now.

Want to be a Data Scientist? Check out the full guide: Road Map to Learn Data Science in 2024 

Let us make 2024 a year of learning, growth, fun, and meaningful work.

Ready to test your skills?

If you’re interested in collaborating, apply to join an Omdena project at: