Meet Dheeksha, Data Analyst
A little bit about me
I am Dheeksha Yogananda, a Data Analyst based in Toronto , Canada.
Can you talk a bit more about your background?
Since I was a child, I was always fascinated by technology and often wondered what life would look like without technology. That started my eagerness to read more which eventually led me to choose computer science as a major subject at university.
I always loved to solve problems and loved programming. In the initial days of my career I worked on Java programming and automations testing like SoapUI, which I did not fully enjoy. In the meantime, the AI field was booming, and I started reading articles about AI capabilities and gradually developed a desire to start working in that field. From then on, my journey of Data Science had begun.
How easy was this transition in your career?
I would say I worked hard for it. I started managing my office work and spared at least an hour or two for my learnings. I enrolled onto different courses ranging from machine learning (ML) to deep learning and started to build a good network of people working in the AI field. I used to constantly ask questions, I interacted with people and started understanding the real problems that AI helped solved. I practiced a lot with Kaggle data and gained an in depth knowledge of the math behind all the ML algorithms. Fortunately, people started recognizing my interest and skill and I was given an opportunity to start my career in Data Science.
What are your strengths in this field?
From my initial assignment, I have been working with textual data and Natural Language Processing (NLP) more prominently which I am enjoying 😊, so I would say handling textual data is my strength.
My analytical skills have helped me solve many problems pertaining to text classification, Named Entity Recognition and language translations.
What do you think is the key in solving any NLP problem?
The data is the key. Of course, no data will be clean and as expected, so we need to devote the initial days of any problem solving to understanding the data, learning it, exploring it and cleaning it. Trust me, the time you spend is worth it. The more correct data that goes into the model, the more precise the results will be.
What do you do day to day?
As a data analyst, my priorities are working on the project assigned to me.
I start my day by checking my tasks and any upcoming deadlines on Jira and plan out how to bring them to closure. I usually have 1:1 calls with my mentor to showcase my improvements on model accuracy and the approach I took to see such improvements. I also work on a demo to be presented at the end of each 2-week sprint as a sign of closing the sprint. I code, raise pull requests (PRs) regularly and ensure that they are reviewed and my main branch is up to date.
I devote an hour every day to reading any new blogs on trending technologies and try to keep myself up to date.
What do you like the most about Haleon?
Haleon has provided me with one of the best teams and an agile way of working which is perfect because it means that I never feed burdened or that my day is too hectic. I have the perfect work life balance here at Haleon.
We usually have monthly casual 1:1 meeting with leads or line-managers, we share any difficult or happy moments in the project which often gears up my mood to achieve more. My colleagues are brilliant because we all help each other. The retrospective meetings in every sprint are a great platform to appreciate each other, raise blockers, and to correct anything for the future. My Line Manager shares any new course links and webinars for my continuous learning and upskilling with ChatGPT through arranging direct meetings with Microsoft.
The most important thing is that we get the opportunity to get stuck in and engaged with any trending technologies like ChatGPT or LLMs. With Haleon, I feel that I am growing every day.
