Tell us about what you do at Crayon Data
As a Senior Data Scientist, I’m involved in research and engineering, which includes formulating, suggesting, and managing data-driven projects geared towards the business’s interests. I also stay informed about developments in Data Science and related fields to ensure that my outcomes are always relevant.
I am super fortunate to work with some brilliant and amazing people at Crayon, who have become close friends. Though most of them are not data scientists, I can still relate to their experiences in the software industry and bounce ideas off them.
What motivated you to the fields of AI and Data Science?
I see myself as a productive, innovative and results oriented person.
“Read a lot! You can’t ever know too much. The more you know, the better position you’ll be in and you’ll be a better version of yourself.” is what I follow. Being from an academic background, I have been teaching, researching, and publishing papers. This helped me keep myself updated on current technologies and trends. Thus, I chose Machine Learning and Natural Language Processing as my PhD domain.
The opportunities for continuous learning and self-mastery are what motivated me to pursue AI and Data Science. It led to a focus on creating business value, leveling my skills up and fed my desire for progress.
The most challenging or exciting part about Crayon
At Crayon, we intend to bridge the gap between academia/research and real-world systems by building enterprise-scale systems to deliver business outcomes and value. The critical part of scalable enterprise systems is framing a data problem, data collection, data engineering, model deployment, scaling, and monitoring. These require us to be adaptable and fast-paced to handle different requirements. It’s both challenging and exciting!
The real challenge arises when our data lakes/warehouse tries to combine unstructured and inconsistent data from diverse sources. Missing or inconsistent data, logic conflicts, and duplicate data all result in data quality challenges. And we solve this by adopting AI-enabled data science technologies like Augmented Analytics and Auto Feature Engineering.
You have a Ph.D. in ML, NLP, Text Analytics and Graph Theory. How has your experience at Crayon helped to enrich your research work?
My role is a mix of research and engineering. As a cross-functional team, we create value across all our projects, and we always give each other clear feedback and support to meet our goals.
Overall, I think working with other Crayons has strengthened my knowledge and understanding across different verticals. My experience with Crayon helped me identify and bridge gaps between theoretical and practical implications of best practices. and putting models to work. It also helped me be in line with the vision of Crayon and its stakeholders.
What do you like most about working at Crayon Data?
Crayon has a strong collaborative culture. Each of us shares the same vision and are dedicated to the mission. I feel privileged to be working alongside open-minded and energetic colleagues. Everyone is unique and has their own set of strengths. What I like most is how we are always learning from each other. We support each other and work together, listen and offer creative feedback and make our ideas a reality. Crayon’s constant appreciation and recognition helps keep us motivated towards our goal.
What is your favorite Crayon value and why?
I like all our Crayon values, and one of my favorite Crayon values is “Responsibility with freedom.”
My first task at Crayon was to work on our consumer choice engine, we were tasked with a brief as well as defined boundaries. We were given the freedom to explore different methods and technologies without restrictions to deliver the outcome with a generalized and scalable framework. This allowed us to pivot and iterate along the way, making it an incredible learning experience for all of us on the team and take the learning and expertise to next set of activities.
Tell us about someone that inspires you.
A.P.J Abdul Kalam, the Former President of India and a world-renowned Space Scientist, has been my inspiration since my school days. Listening to his speeches & lectures with quotes were motivating, courageous, and empowering. Among his many books, his autobiography, Wings of Fire, has motivated me throughout my personal and professional life.
How do you think we can encourage more women and girls to join data science, AI, and related fields?
Women are the largest untapped reservoir of talent in the world. This industry is great for those with curious minds. Working with data allows us to play with knowledge. We should encourage Women not just in STEM, AI, and Data Science, but across all domains of knowledge. For anyone interested in data science, I would encourage them to make use of open-source online resources.
“And build your network! It’s all about who you know!”
But really, it is. And this quote was not something I actively devised because I knew it would help my career. It came organically from interactions with current and former co-workers, students, and the result of attending and presenting at meetups and conferences and doing courses online.
Here are some of the resources I used that helped me, and I would suggest for Data Science aspirants,
- Clean Code
- Clean Coder
- The Pragmatic Programmer
- Factoring: Improving the Design of Existing Code
- Designing Data-Intensive Applications
- Kaggle’s free micro-courses
- Deep Learning by Ian Goodfellow
- Machine Learning For Dummies
- Nine Simple Steps For A Better-looking Python Code
- Data Science – Harvard
- Full Stack Deep Learning
- Dive into Deep Learning
For Machine Learning aspirants,
- “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- “Learning from Data” by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin.
- For Calculus: 3Blue1Brown’s YouTube playlist (amazing channel in general!)
- For Linear algebra: Linear algebra course on MIT by Professor Gilbert Strang.
- Reaktor’s and the University of Helsinki’s AI course
- Machine Learning Crash Course
- Approaching (Almost) Any Machine Learning Problem– by Abhishek Thakur
- Machine Learning and Data Engineering Courses – by Srivatsan Srinivasan
To build a solid foundation in data science, read and apply your knowledge by participating in competitions (Such as Kaggle), hackathons, and contributing to open-source projects.
Any final words?
Never Stop Learning! (ஓதுவது ஒழியேல்)