1. The Spark: How I Landed My First Internship at Tredence
I remember the morning of April 2024, when I was about to start my journey at Tredence. A little nervous, quite excited to be a part of one of the fastest growing AI companies and curious – to see what my internship holds. As an engineer majoring in Data Science, I was naturally drawn to ML Ops and Automation, two areas I hoped to learn more in my internship. I believe in a career path fueled by passion projects, for me, one such learning curve was the fact that no more “assume the dataset is clean;” the data was messy, the challenge lies in analysing the right dataset, fit for automation pipeline.
The first journey ahead of beginning day 1, was the AI intern phase from a meek third year student to a confident interview attendee. I heard rave reviews from my college seniors about their time at Tredence, how they were involved in projects with SQL dashboards, automation and data engineering. I applied via my campus committee and got shortlisted. It is good to build a resume that talks about our projects, relevant skillsets and interests. My interview experience was smooth, and interesting: they actually wanted to see how I would fit within the data team!
Thrilled at the premise of this promising career at one of the leading AI Companies, I packed my bags to Bengaluru. The first week was a roller coaster, but what I loved the most was an open-floor culture. Ideation happened in hallways, in the cafeteria and ofcourse, whiteboarding sessions. It felt like an extension of college learning—but with real-world tools and impact. Two years in, little did I know that my three-month internship would be the beginning of a data scientist career.
2. From Theory to Reality: My First 90 Days as an Intern
The daily life as an intern was fast paced, filled with learning from real-world projects. For those looking for a data science career progression, aligned with experimental learning, Tredence’s data team is the place to be.
The first tool that hit me was Python. I had only written simple scripts for assignments, but here I was cleaning a 2-million-row customer transaction file using pandas. I still remember spending an entire afternoon stuck on a group by error; my mentor just smiled and said, “Google is your best friend, Pooja” By the end of week three, I had built my first automated report that saved the team two hours every Monday. That tiny win felt huge.
Even as an intern, we are given responsibilities as a part of a project and expected to manage our deadlines. I was initially scared to open up in meetings and share ideas, my mentor Shravan, was very supportive and always encouraged to share thoughts – he believed, no idea is too small, and that experiments are how we keep the curiosity alive. Slowly, I witnessed my career growth from a shy intern to a confident data scientist.
Then came SQL. I thought I was decent at queries until I had to join six tables with millions of records and optimise it, so the dashboard didn’t crash. My first query took 45 seconds to run; after my teammates showed me how to use indexes and CTEs, it dropped to 4 seconds. I literally did a happy dance at my desk.
For visualisation, we used Tableau and Power BI. I created my first interactive sales dashboard in Tableau during week five. The client loved the filters and drilldowns, but I had to redo the entire colour scheme three times because “the client’s brand colours are non-negotiable.” Power BI came in later when we needed to embed reports into their internal portal – learning DAX felt like learning a new language overnight.
A crucial learning that I believe everyone should remember – test theory practically. Never assume and always check your codes at random to get a different perspective. I’m often asked, “is there a set formula for data science career outlook,?” and the answer is, yes, constant hunger to keep experimenting.
3. Key Projects & Breakthroughs: Where I Started Adding Real Value
As an intern, every time I built something that impacted real-life challenges and turned them into solutions, I felt a kick! An early project I did for a retail client involved a predictive analytics model. I built it using Python and improved the model’s accuracy from 57% to 82%.
The next was a customer segmentation dashboard – this time, I had to leverage Tableau to create meaningful segments. It is always fun working on Tabelau to visualize reports. It’s beautiful to transform raw data into something tangible, meaningful is the reason I became a data scientist. I analysed the purchase behaviour, demographics and built a dashboard with interactive visuals. The client’s marketing team loved the visual representation, and it enabled them to run targeted campaigns easily. It was my first time seeing how data visualisation can directly drive business decisions. This was the day I knew that my career growth was in the right direction.
The biggest challenge I overcame was self-doubt. Due to the fear of failure, we often filter ideas and avoid experimenting. "Through my internship at Tredence, I realised that with data, if there’s one thing that truly yields results, it is endless experimentation – tweaking features, trying different algorithms, and constantly iterating is what turned average models into impactful ones.
The early wins as an intern gave me the confidence to pursue a full-time career as a data scientist. Towards the end of my internship, I stayed updated with the open roles at Tredence and applied, when there was an opportunity in the retail team that built Gen AI automation, needless to say, the rest is history. Nearly two years in, I still grin wide like the doe-eyed intern that I was.
4. The Leap: Transitioning from Intern to Data Scientist at Tredence
Working as an intern sure offers a preview into the world of full-time work, however, the interview process was rigorous, and the team wanted to ensure I’m game for the challenges that would come my way. I had two technical rounds, where I had to work with Python, SQL and a cloud platform. The most important angle is to showcase our thought process. As a data scientist, career growth often depends on our ability to question why this method, why not the other way around.
My next round was a fun interaction where I had to present my hypothesis, showcase my analytics model and we had an open discussion around the role – what it entails, where I would likely fit in the team.
The transition from being an intern to a full-time Data Scientist was smooth. My responsibilities significantly shifted from handling sections of a project to owning it end-to-end. From initiating business interactions to setting up stakeholder expectations, project management, to dataset mapping and deployment, I now take care of the full lifecycle. As the project complexities increased, I started collaborating closely with cross-functional teams including marketing, product, and engineering stakeholders to deliver impactful solutions.
A flagship project in the first six months of my full-time career was building an advanced prediction model using Python, SQL and ML pipelines. A stark difference I observed was that my confidence had grown by leaps and bounds. I am a tester, coder, theoretical specialist and practitioner. Felt like a super-hero running everything end-to-end. This is the career growth, I had dreamt of. Feels surreal living it.
Over a period of time, my stakeholder management, soft skills, and most importantly, presentation skills greatly improved. I understood how to showcase whiteboarding sessions into use cases.
As a fresher, there were several learning opportunities that came my way. Tredence offers something called “Skills Passports,” which helps us mapping our skills to the most relevant projects. We are encouraged to learn endlessly, with the ability to deepen one’s expertise in data engineering, data science, prescriptive and generative AI by accessing our cutting-edge learning tools. There is a Data Science Grad Program, where Data science graduates participate in an intensive 14-week program to develop their expertise and fastrack their career growth.
5. Reflections & What’s Next: Lessons I Wish I Knew Sooner
I get asked one question constantly: What is a fundamental skillset to have as a data scientist. I would say it is being able to have a method to madness. By madness, I mean datasets, our code, the libraries; one needs to understand how to approach it, have their own categories. Every day, add notes on what you’re working on, and keep the project download clean.
Another habit I swear by is networking; meet with folks from other teams, network internally. And ask questions – because someone somewhere has either already faced it or is facing the issue. Easier to solve challenges in groups. I also realised the importance of balancing speed versus perfection — delivering a good solution quickly is often better than chasing a flawless one that never gets deployed.
I have never for once wondered “Why Tredence,” it was almost natural to me thanks to the internship experience. What sets it apart is the culture and a strong focus on AI/ML learning for data scientists. Working here has given me opportunities to learn cutting-edge technologies while contributing to real business impact every single day.
To all current interns and freshers, be proactive, ask for feedback regularly, and treat every small task as a learning opportunity.
Today, I’m really excited about my role as a Data Scientist at Tredence and look forward to leading a data science pod in the near future.
Got Data Science on your mind? Come, join Tredence:
