Sometimes we forget the humans behind the tech in our ever busy world. DSF is fortunate enough to know some incredible tech leaders across the world and has the privilege of hearing them present at our events. That being said, our Speaker Spotlight sets the stage to get to know our speakers on a more personal level and connect them with our growing community. Read the mini interview below!
A bit about Jayendra:
Jayendra is a Data Scientist at Mars Petcare and has been in the industry for over 2 years. Over this time period, he has worked on a variety of projects from assessing coral reef restoration efforts using computer vision, geospatial analysis to acquire veterinarian practices, and A/B testing to assess an assortment optimiser for a major retailer.
How did you start out in your tech career?
My journey in data started at university when I took a Statistical Data Analysis course that took me from the basics of stats all the way to neural networks. After that, I was hooked! I started self-teaching myself Python and landed a graduate scheme at Mars Petcare as a Graduate Data Scientist.
What are the signs of success in your field?
In my opinion, success is a personal thing and can be measured both intrinsically and extrinsically. People have all sorts of reasons for doing things in their jobs and lives. It’s best to see how you’re doing based on your own goals rather than trying to copy someone else. With that being mentioned, I think the signs of a successful data scientist would be:
– Constantly learning – In a rapidly evolving industry like data science, data scientists need to foster a culture of continual learning and experimentation. Progressing consistently while maintaining strong foundational knowledge and being receptive to new ideas makes staying ahead of the curve more
manageable, ultimately contributing to creating more value as a disruptor.
– Able to understand the problem/business deeply – Every business and the problems they face are unique, and being able to translate a business problem into a well-defined data problem and creating an effective solution around that, whether it’s simple or complex, plays a key role in reaching success.
– Sincere and humble – It’s easy to get frustrated when you don’t know something. Especially in a field like data science, where there’s so much to learn and the landscape is constantly evolving, so it’s important to be recognise the extent of your capabilities and be open about them.
What is the best and worst thing about your job role?
I think the best things about the job role are definitely the people and the problems. There are so many smart people I can learn from, get advice from, and have interesting discussions with. The problems are also incredibly diverse. From various functions like marketing, supply chain, and health, to specific data science fields, like time series, deep learning and natural language processing to name a few. I love being able to take a business problem, discover insights like a “data detective”, and then create a final solution.
The worst thing about my job is it’s not always immediately quantifiable. Sometimes you’ll spend a large amount of time generating insights or building the best model, and you must wait a while to see how your analysis/model affected the business. As someone who loves to see data and results, and learn from it, it can be demotivating.
What can you advise someone just starting out to be successful?
How do you switch off?
I like to switch off by going to the gym, playing video games or playing my guitar. These activities help me to take a step back, rest and recharge.
What advice would you give your younger self?
Do more things that scare you. It’s easy to get comfortable and stick to what you know. Growth always happens outside of your comfort zone, so go explore, see, and try everything you can.
What is next for you?
I’m excited to be continuing as a data scientist, learning new techniques, and applying them to business problems, but aside from that I have no concrete long-term plans. I’m still early in my career, but I think it would be fun to lead a team in the future.
If you could do another job now, what would you do? Why?
If I could do anything, I’d love to be involved in some of the state-of-the-art AI projects like LLM(Large Language Model) and LMM (Large Multimodal Model) development and robotics projects like Atlas and Spot, that they build at Boston Dynamics. I think it’s really cool seeing how quickly technology is evolving, and would love to be directly involved in building cutting-edge tech.
What are your top 5 predictions in tech for the next 5 years?
1. As more and more models become deployed, and get more complex, there’ll be a greater need for MLops best practices, including model validation, explainability, and monitoring to prevent bias, assure fairness, and ensure that the model is working as intended.
2. There will be a greater focus on Data Analytics skills and data literacy for everyone (including those not in data/technology roles) as more companies move towards data-driven approaches.
3. AI-enhanced working. More and more people will be using tools like ChatGPT, and DALL-E to iterate quickly, whether that be coding, creating references for art, or drafting documents. Rather than replacing roles, AI tools will help us work faster and more efficiently.
4. There will be an increased focus on sustainable and renewable energy technologies, such as advanced solar panels, and more efficient wind turbines (and possibly nuclear fusion?)
Watch Jayendra’s session at the Data Science Festival hereThank you to all our wonderful speakers for taking part in our Speaker Spotlight!
Want to become a DSF Speaker? Apply here!