Rohan Chikorde is a Data Scientist in one of the world’s leading food company – General Mills. He has completed his Computer Engineering from G.V.I.T, Mumbai and holds a Post-Graduation in Data Science from S.P Jain School of Global Management. In General Mills, he is working in the core Data Science team working on multiple projects based on Advanced Machine Learning, Natural Language Processing, Visualization etc. Along with his own projects, he is also selected to lead Deep Learning research projects for General Mills, India. Love for mathematics and passion to learn always new things leads him to the field of Data Science and Artificial Intelligence. He has worked with big multinational companies like Accenture and General Mills as well as had an opportunity to work in start-ups like G-Square solutions and Light Information Systems. From mischievous student in school and college to today’s serious Data Scientist in his current life, his journey to become Data Scientist was full of hard work and passion. Rohan was first exposed to Analytics in Accenture. In his Post-Graduation, professor who had big impact on Rohan was Satish Patil in S.P Jain School of Global Management. Rohan was fascinated with the underlying mechanics of the brain and algorithms that give rise to all our thoughts, dreams and desires, and our attempts to replicate those mechanics. He is very passionate about his career and truly believed if you choose your career that you love the most, then it’s just a game that you have to play smartly to stay on top.
How did you get into Data Analytics? What interested you in learning Data Analytics?
Rohan Chikorde: Though I started my career with Accenture as an Analyst, unfortunately it was not a proper Analytics profile for first few months. I used to do kind of monotonous job. After working for first few months only, with all due respect for all kind of jobs I realised that I cannot do similar work for rest of life. That’s when I decided, I will not work for any monotonous job profile again in my entire life where you don’t have use your brain. And that’s where search for something new field started. Fortunately, within next few months only, new project came in Accenture and for which they need three Analytics freshers and I got selected. This is the project where I got exposed to Analytics and client was Facebook.
From here, my journey to become Data Scientist started. Then, I started reading more about Analytics and Machine Learning. While working in this project, realized the power of data and why analytics is important in business. After working there for more than 2 years, I decided to take my knowledge of Analytics and Data Science to next level. Hence quit my job and did post-graduation in Data Science where I enjoyed actual mathematics behind the machine learning algorithms.
What was the first data set you remember working with? What did you do with it?
Rohan Chikorde: In Accenture, I worked on multiple data but the data set that I still remembered was from the Machine Learning class in S.P Jain School of Global Management by Professor Satish Patil. He took a small data set of only 10 rows and showed us how to build predictive model using Machine Learning algorithm on paper. In that class, using that small dataset we built multiple Regression and Classification models using pen and paper. There I learned the actual mathematics behind the algorithms and this technique of learning new algorithms using paper and pen still rooted in my blood. It was a great fun and great learning experience.
Was there a specific “aha” moment when you realized the power of data?
Rohan Chikorde: I realized the power of data in my first job itself when I had an opportunity to work for Facebook’s project. But soon realized, if I don’t know how to correctly use that data, then it’s just like a strong sword in your kitty but you cannot use it for cutting vegetables. However, my first “aha” moment was in the class of Machine Learning. When I learned to build predictive models by hand on paper and after understanding the mathematics of these algorithms I know how to apply on large dataset. Later while working on multiple business problems, I realized the power of both data and algorithms in my kitty.
What is your typical day-in-a-life in your current job. Where do you spend most of your time?
Rohan Chikorde: No two days are same in this field. There is always a constant stream of getting data and looking data from different angles using visualization and defining different approaches to solve the business problem. Most of time I spend interacting with clients and understanding their thoughts and vision for the project and then bringing that vision into reality.
How do you stay updated on the latest trends in Data Analytics? Which are the Data Analytics resources (i.e. blogs/websites/apps) you visit regularly?
Rohan Chikorde: I read a lot and there is lot of information flowing on internet and it continuously increasing. Hundreds of people posting their findings and inventions on social media like Facebook, Twitter, YouTube and Linkedin. Along with that, I follow KD Nuggets, Machine Learning Mastery, Analytics Vidhya, Digital Vidya, DeepMind blogs, Fast.ai. Also, I do courses on Coursera, Data Camp, Edx, Udacity etc. By now, I have completed 16 certification courses, and many are in pipeline.
Share the names of 3 people that you follow in the field of Data Science.
- Andrew Ng
- Geoffrey Hinton
- Kirill Eremenko
And list goes on ….
Team, Skills and Tools
Which are your favourite Data Analytics Tools that you use to perform in your job, and what are the other tools used widely in your team?
Rohan Chikorde: In General Mills, we use lot of tools depending on task. For Visualization, we used Tableau. For IDE purpose, we use CDSW, Pycharm, IntelliJ. From the modelling perspective, we use R and Python. Now for Deep Learning, we started with Keras, Theano, Tensorflow. And Excel for small data and quick analysis.
What are the different roles and skills within your data team?
Rohan Chikorde: My team comprises of Data Scientists, Data Engineers, Big Data Architects and BI/Visualization Experts which brings some diverse skill sets required to deal with structured and unstructured data at a scale. We also have different reading and practitioner group where everyone shares knowledge or road blocks based on their projects and entire team do brain storming on any road blocks or learns new thing if any.
Help describe some examples of the kind of problems your team is solving in this year?
This year we are working on multiple projects and couple of projects are in pipeline. To name a few:
- Product Demand Forecasting
- Online Sales Forecasting
- New Item Forecasting
- Text Analytics for product description using NLP
- Anomaly Detection in Sales
- Strategic Revenue Management
- Product Recommendation
- Deep Learning / Reinforcement Learning
And many more….
How do you measure the performance of your team?
Rohan Chikorde: We believed in 360-degree feedback mechanism to fill in the gaps in terms of learning and setting up new bars. However, we do also have comprehensive appraisal cycle where individuals are judged on following metrics:
- Impact on business through hard saving or soft saving
Advice to Aspiring Data Scientists
According to you, what are the top skills, both technical and soft-skills that are needed for Data Analysts and Data Scientists?
Rohan Chikorde: I believed, to become good Data Analyst or Data Scientist, you need to be strong in following four categories:
- Mathematics & Statistics
- Business Knowledge
- Programming and Visualization
- Ability to explain results to business
How much focus should aspiring data practitioners do in working with messy, noisy data? What are the other areas that they must build their expertise in?
Rohan Chikorde: In real life projects, data will always be messy and noisy. Data Preparation is one of the important step in any data science project. I spent a lot of time on data cleaning and data preparation even before touching machine learning algorithms. There are even libraries and techniques specifically built for Data Processing and Data Preparation. Along with that, it is important to have statistical knowledge to apply on top of it to make sense of data.
What is your advice for newbies, Data Science students or practitioners who are looking at building a career in Data Analytics industry?
Rohan Chikorde: First thing I would say, if you are passionate enough to learn new things every single day then only go ahead in this career. Constant learning is the necessity of this field and to build career in Data Science, you may follow the following steps:
- Focus on linear algebra, statistics and mathematics. Understand the concepts behind machine learning and deep learning algorithms.
- Learn Python, R programming or any other analytical tools to efficiently play with data.
- You should know how to draw insights from huge data. Hence learn visualization tools like Tableau, Power BI, QlikView, Spotfire.
- Build skills of articulating results properly to business.
- Learn about Big Data tools.
- Participate in Kaggle competitions or build different projects on your own.
What are the changing trends that you foresee in the field of Data Science and what do you recommend the current crop of data analysts do to keep pace?
Rohan Chikorde: I believed Artificial Intelligence, Deep Learning and Machine Learning have a capability to impact positively every business around the globe. There is an enormous opportunity in India and around the globe for the practitioners. The field of data science is in the initial phases and this would evolve a lot based this cross-domain interaction.
Would you like to share a few words about the work we are doing at Digital Vidya in developing Data Analytics Talent for the industry?
Digital Vidya provides a great platform for all data science aspirants. Corporate trainings, courses and webinars are worth investing time for trainees as well as companies. It brings diverse knowledge of Data Science under one platform. Keep up the good work!!!
To know more about Rohan Chikorde, you can check out his LinkedIn profile.
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