Learnings, tips, and inspirations from a personal success story of getting a data science job offer at Accenture.
With Apoorv Mehta
Can you describe your journey into data science?
It has been a roller coaster ride for me venturing into the data science space in the past 4 years. I still remember the day when I was a trainee in Tata Consultancy Services (TCS) back in the year 2015. I came across the term Sentiment Analysis and I got really excited to see the kind of workaround Twitter sentiment analysis.
It was that day when I initiated my thought process of getting a job in data science.
However, little did I know about the kind of skills you require for securing a job as a data scientist. Lack of guidance coupled with unawareness around this field delayed the process somehow.
However, I was mentally well prepared for any challenge that could come my way. Thus, I decided to go for a Master’s in Business Analytics after having gained 4 years of experience in a non-analytics position. This was my first key milestone so to speak. The second big milestone that I felt was securing an internship with one of the biggest hospitality chains and building a predictive model from scratch for them. This was my first live project in machine learning. Last, but definitely not least, spending 8 weeks with Omdena as a Machine Learning Collaborator was another key milestone in my data science journey.
What skills helped in particular?
Omdena proved to be of great help during my interview process with Accenture. The job description for which I was interviewed covered mostly technical aspects of data science such as Natural Language Processing (NLP), various theoretical aspects of machine learning, and many more. It also demanded soft skills such as teamwork, communication, and self-drive. Luckily, the project I worked on during my stint at Omdena involved drawing insights and recommendations using NLP. This was of great relevance while I was being interviewed with Accenture. The interviewer was very much interested in knowing about Omdena and also the kind of work and effort I put into the project I was a part of.
How did you prepare for the interview?
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 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?
What tips can you give someone being in a similar position of entering the job market?
I would suggest starting applying for good analytics projects right away, even in your first years, and to make every day count by learning something new.
It is a beautiful journey and trust me, the amount of efforts you make during this phase is directly proportional to the quality of job you get somewhere down the line.
How did you prepare your CV and project portfolio?
I personally stay very active on LinkedIn and I have always been in the process of connecting with people who have been doing great in the data science space. And I have not hesitated a bit in seeking help in building my profile/CV from some of them. If they don´t help you build your profile from scratch, they definitely help you in guiding important pointers that you can put in your CV in order to make it ready for the job market.
In this way, my tip is:
Reach out to people on LinkedIn and just ask them in a friendly and gentle way if they could guide you a little bit. You can offer something in return like sharing one of their articles, etc.
As already said, while having a well-structured CV is crucial, securing exciting and relevant real-world data science projects is equally important. This shows that you are ready for learning new skills and puts your profile in the front seat for any particular job. So, I would request to apply on platforms like Omdena to build and diversify your project portfolio.
Any other tips that were pivotal for you
Career transition in data science as in my case requires a lot more perseverance and composure than one thinks. So, it is of vital importance that during this process, you stay calm and self-motivated. This not only applies to data science jobs at Accenture but any other company where you can do meaningful work.
Do not get bogged down by the rejections that you might face.
This reminds me of a quote from a great book I read:
“Obstacles are necessary for success because in all careers of importance, victory comes only after many struggles and countless defeats. Yet each struggle, each defeat, sharpens your skills and strengths, your courage and your endurance, your ability and your confidence and thus each obstacle is a comrade-in-arms forcing you to become better… or quit. Each rebuff is an opportunity to move forward; turn away from them, avoid them, and you throw away your future.”
Sometimes, companies do not look for the right skills, they look for the right fit. So, keep up the spirits high.
ALL THE BEST!