Shweta Gupta, VP – Technology at Digital Vidya: Data Science can be intimidating and complex to understand for a non-techie. But since its a hot cake right now, everyone wants to know a bit about this space. I get these queries very often, “How do I understand the work going on in the Data Science Industry?”. My response to this is short & simple, go and read the interviews of top Data Scientists. And honestly, interviews are a great way to be on your game with the latest in the space. It’s an exciting way to be updated even for senior professionals in the field as there is always a new thing to learn from your fellow professionals from different industries who work on different challenges altogether.
Talking to a senior professional like Jyoti Vaddi who has cut her teeth as a Marketer along with using Data Science to her credit was interesting and the information she came up with was pretty intriguing. In this interview, we learn about Data Science from a very different lens, the lens of a Marketer. That’s something new to our Data Science Industry Insights Series, but a lot welcome. Let’s move and read what she has to say.
Jyoti Vaddi’s 7 years of professional journey has been quite a swift and versatile ride – it revolves around Data Science, Customer Success, Product Management, Business Development, Marketing and Sales – in industries like Banking, Insurance, Retail, Technology, and now Agriculture.
After graduating from BITS Pilani, her first career stint was with J. P. Morgan Chase Bank where she worked in a hybrid role entailing Client Servicing, Operations and Product Management.
Thereafter, Jyoti worked with two coveted analytics services companies – Mu Sigma Business Solutions and EXL Consulting. She worked with clients from varied domains across geographies and was constantly solving business problems for stakeholders from different business functions like Supply Chain, FP&A, Marketing, Sales and Operations. She also started leading teams quite early in her career – from training to performance management. At both these organizations, Jyoti believes that her professional learning was exponential – like one of those cumulative distribution functions. Around the same time, she had also taught modules of the Business Analytics course at an Engineering College in Bangalore.
Jyoti gathered her first field experience in a Sales and Customer Success role in an early stage startup. This stint made Jyoti challenge her comfort zone – from patient pursuits to harsh rejections.
Right now, she continues to work in the startup ecosystem and in her current role at CropIn Technology Solutions, Jyoti Vaddi heads the Marketing and Business Development functions. CropIn is an Ag-Tech startup that operates in 20 countries and connects the entire Agri-ecosystem to enable data driven farming. They harness technologies like Artificial Intelligence, Geo-tagging and Satellite Monitoring to revolutionize the Agri-ecosystem.
How did you get into Data Analytics? What interested you in learning Data Analytics?
Jyoti Vaddi: Soumithra Ramesh, my friend from BITS, had introduced me to the field of Data Analytics – this was back in 2009. She helped me understand the potential of the field and the career map that I would possibly have for myself. It was through her that I ventured into the field. And, I am glad that I took her advice. I want to take this opportunity to thank her in this regard. 🙂
Data Analytics excites me for the following reasons –
- the challenge of defining the exact problem statement
- the structured thinking that it demands for the problem-solving process
- the immense opportunities of application in every domain and business function
What was the first data set you remember working with? What did you do with it?
Jyoti Vaddi: My first project taught me the applications of Cluster Analysis. It was a project to build a model that measured the productivity of sales associates across all the big-box format stores for a large home improvement retailer based in the US. My first dataset that I was given, was also one of the largest datasets that I have ever worked with.
Was there a specific “aha” moment when you realized the power of data?
Jyoti Vaddi: At the end of every client presentation, the projects that I have worked and delivered upon always had the ‘WOW’ factor because all the problem statements were so very different – from attribution models in digital marketing to sales forecasting. There was always something new to learn – either about the domain/business function or the technique to be applied.
What is your typical day-in-a-life in your current job. Where do you spend most of your time?
Jyoti Vaddi: I usually start my day by reading business and tech news. Then, proceed for a huddle with my teams – Marketing and Inside Sales – to plan for the day. My Inside Sales and Marketing teams work across geographies – from Asia to Latin America. Thus, they always have new challenges and learnings about the markets that we operate in. This makes the huddle quite interesting, and I invest nearly 20-25% of my time in these daily huddles.
Thereon, I invest rest of my time in driving execution, meeting with internal and external stakeholders – CXOs, Sales, Customer Success, Product, HR and PR, resolving operational complexities, strategy discussions, coaching, hiring and a lot more
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?
Jyoti Vaddi: I enroll myself in a lot of online courses – from specific analytics techniques, marketing, product management to content writing – to pick up skills that help me in my role.
I listen to podcasts on Castbox, specifically follow SuperDataScience.
Some other resources that I read and refer to, particularly for Data Analytics are:
- Data Science Central
- Analytics India Magazine
Share the names of 3 people that you follow in the field of Data Science or Big Data Analytics.
- Andrew Ng
- Bernard Marr
- Kirill Eremenko
Team, Skills and Tools
Which are your favorite Data Analytics Tools that you use to perform in your job, and what are the other tools used widely in your team?
Jyoti Vaddi: My toolkit primarily comprises of R, SAS, SQL, Excel and Tableau. At CropIn, our data science team utilizes R, Python Scikit, TensorFlow and the likes.
What are the different roles and skills within your data team?
Jyoti Vaddi: We have DW Engineers, BI Engineers, Data Scientists and ML Engineers in our team. We also have Remote Sensing Specialists who work on the satellite data.
In agriculture, the crop output depends on many different variables and hence the potential applications of big data and machine learning are immense. Our BI Engineers work with Jasper, D3.js and the likes. On the other hand, ML Engineers have experience in deploying random forest and deep learning algorithms.
Help describe some examples of the kind of problems your team is solving in this year?
Jyoti Vaddi: Farm digitization, Farm advisory and Traceability in the Agri-ecosystem are the problems that we are currently solving in the Ag-Tech space.
We have digitized 3.1 million acres of farmland while working with 3,500 crop varieties and assembled 700 million data points. As of 2017, our platform has gathered 88 TB of Agri data. The Agri data size is estimated to be 25 Petabytes by 2030, with an assumption that 50 percent to 60 percent of the farms are digitized.
We work to enhance per acre value by leveraging the Agri data to enable farms to generate their own scorecards, to assess performance and prescribe the next steps based on a crop health score and learnings from other connected farms in the network.
Our algorithms make the farms self-learn from the data generated on the network every season and plan to achieve better the following season by comparing seeds, inputs and practices that have given better results on the network by saving cost, improving yields, fighting disease and pest attacks.
How do you measure the performance of your team?
Jyoti Vaddi: The top 3 performance criteria in our DS team are:
- Ability to explore all perspectives of Agri data (even without a problem statement)
- Prediction Accuracy
- On-time project delivery
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?
Jyoti Vaddi: Right attitude, zeal and ownership – these will aid you in performing well in any job role. Needless to say, communication skills are a must-have. You should be able to quickly connect the dots and explain the utility of the insights generated to the business.
Statistical bent of mind and constantly upgrading yourself with trendy programming and visualization skills will equip you with the right technical armor to fight your battles with the given problem statement. Read up on the latest in the field and that will help stay updated.
Some skills to consider having are:
- Data Programming – R, Python Scikit, SAS
- Data Visualization – Power BI, Tableau, Qlikview
- Experience in deploying machine learning techniques will you give an edge
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?
Jyoti Vaddi: One will not always receive a tidy and straightforward dataset to work upon.
- Ask the right questions
- Identify the factors
- Eliminate the noise
- Make the appropriate assumptions to fix the gaps
Though the process of working with messy and noisy data can be maddening, however the end result will help you gather confidence to face such data again.
Building domain knowledge and functional expertise will come handy in quick understanding of similar problems in the future.
What is your advice for newbies, Data Science students or practitioners who are looking at building a career in Data Analytics industry?
Jyoti Vaddi: You should be working on your lateral thinking skills to approach problems. One can master tools and techniques with adequate opportunities of using/applying them.
On the data mining and programming side, attack the open source stack first – SQL, R, Python. This will open up opportunities with a lot of organizations – from large enterprises to SMEs.
You can work in the field of Data Science for a plethora of companies – Analytics Boutiques, Consulting, IT Services, Captive units or Product organizations. Each of these will offer you a different career experience.
The first three types of organizations will give you an exposure to a variety of business problems across industries whereas captive units and product focused organizations will offer domain expertise and challenge you with industry specific problems – sometimes first of the kind in the industry. You should be making your choices after self-assessing your strengths and aspirations.
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?
Jyoti Vaddi: AI-enabled technologies will be creating newer jobs (and eliminate some existing ones too) in the next 3-5 years, and hence it is important to keep upskilling yourself to stay relevant and be considered for the sought-out project/job.
My recommendation? Read, Upskill, Practice, and Go Grab Opportunities.
Would you like to share few words about the work we are doing at Digital Vidya in developing Data Analytics Talent for the industry?
“Digital Vidya offers a launch pad for all the data science aspirants to explore and be a part of the revolutionising field. It is paramount to have these training platforms which identify the industry demands and craft out learning opportunities for the seekers.
Digital Vidya’s efforts in the talent creation and nurturing space for the Data Analytics industry is commendable. Wishing success to the team.”
To know more about Jyoti Vaddi, you can check out her LinkedIn.
Are you inspired by the opportunity of Data Science? Start your journey by attending our upcoming orientation session on Data Science for Career & Business Growth. It’s online and Free :).