Deebyadeep Parida leverages data to bring the stories he tells to life has been saved
Deebyadeep Parida leverages data to bring the stories he tells to life
Deloitte AI Institute is proud to introduce a series profiling AI warriors who are pushing the boundaries of what’s possible in the search for new and innovative uses of AI.
Can you share the most interesting part of your career journey?
I have always hoped to work as a teacher. I have had several opportunities to train individuals courtesy of the Artificial Intelligence Guild. I have always found great satisfaction in inspiring and assisting others in achieving both their personal and career goals. This has been true ever since I began teaching in AI academies and taking on roles as a mentor at a variety of academic institutions.
I have always found great satisfaction in inspiring and assisting others in achieving both their personal and career goals.
During my time as a developer for over eight years, I have taught advanced analytics, and coached and supported those I have mentored as they explored a variety of career opportunities. My time as a mentor has taught me a great deal as I even have been introduced to a variety of new domain material by my mentees who often serve as subject matter experts in their respective fields.
What excites you most about working with data and AI?
I adore using data to tell stories and to help give those stories life. I provide recommendations as a data scientist to help a business better its goods, services, and consumer experience.
I was primarily incentivized to become a data scientist by my drive to assist others.
In data science, this can involve assisting a business in better understanding its clients or enhancing the manner in which it leverages client data. I find incredible fulfillment in my work when I see its multitude of significant and real impacts.
Describe an interesting project that you have worked on.
In the real world, the data science process flows beyond data processing and the training of predictive models. In a production system, the data science process is heavily invested in data and feature engineering, model delivery, and governance. As an example, for a large-scale media client, we are working on developing a highly effective and reliable AI platform, which will prove essential for the business’ data science processes.
Data technologies are fast expanding both in breadth and magnitude. As such, we are exploring potential opportunities for scalability in the future. The aforementioned platform was engineered with individual components and management as a separate service in mind. In addition, each individual component of the platform was designed to function flawlessly and have the ability to seamless integrate with other solutions.
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