Different environments.
The same analytical
mindset.
My experience has taken me from scientific research to my first corporate environment — giving me different perspectives on how data, processes and people come together to solve problems.
Two environments.
One analytical mindset.
At IIRS — ISRO, I worked in a highly structured research environment where precision, methodology and patience mattered.
At Dow Chemical, I experienced my first corporate environment — where analytical work had to connect with processes, stakeholders and practical business outcomes.
The environments were different. The underlying lesson was similar: good analysis is not only about technical capability. It is about understanding context and communicating what the information means.
Dow Chemical
My first corporate experience gave me the opportunity to work across process analysis, data analytics and business improvement — learning how structured analysis can support decisions in a complex organisation.
Process Analysis
Worked on process mapping and structured analysis to understand existing workflows, identify gaps and highlight opportunities for improvement.
Customer Analytics
Worked with customer feedback data and analytical workflows to identify themes, patterns and areas that could support improved decision-making.
Process Improvement
Used structured problem solving to examine process quality and identify opportunities for more efficient and consistent workflows.
Stakeholder Management
Learned to work across different stakeholders, understand their perspectives and translate conversations into structured analytical work.
What I learned personally
Dow was my first experience of working in a corporate environment. One of the biggest lessons was realising how much learning happens through people — asking questions, observing how experienced professionals approach problems and being open to feedback.
I also learned that corporate work is rarely perfectly defined. Sometimes the problem becomes clearer only after conversations with the people closest to it. That made adaptability and communication just as important as technical analysis.
Beyond the work itself, the people I met made the experience memorable. The openness, approachability and friendships I found there reminded me that a good professional environment is also one where people are willing to help each other learn.
IIRS — ISRO
At the Indian Institute of Remote Sensing, I worked with hyperspectral and geospatial datasets, using Python and analytical methods to turn complex scientific data into structured insights.
Spectral Analysis
Analysed large spectral datasets across multiple bands to identify patterns and distinguish between different material types.
Data Pipelines
Used Python-based data processing workflows to clean, structure and analyse datasets efficiently.
Machine Learning
Applied analytical and machine learning techniques to support classification and pattern identification within scientific datasets.
Research Communication
Learned to communicate analytical findings within a research environment where precision and methodological clarity were essential.
What I learned personally
IIRS taught me the value of structure and rigor. Working in a research environment meant that assumptions had to be questioned, methods had to be followed carefully and results needed to be supported by evidence.
I also learned patience. Scientific and analytical work does not always produce an immediate answer. Sometimes the most important part of the process is understanding the data well enough before attempting to interpret it.
Most importantly, working around people with deep technical knowledge taught me how much there is to learn by simply being curious, asking questions and paying attention to how experts approach difficult problems.
Three things
that stayed with me.
Structure
Good analysis needs a method. Whether the problem is scientific or business-oriented, a structured approach creates clarity.
Adaptability
Real problems rarely arrive perfectly defined. Being comfortable with ambiguity is part of becoming a better problem solver.
People
Technical skills matter, but much of professional growth comes from the people you work with, learn from and learn alongside.
Data can tell you what happened. People and context help you understand why.
That is the perspective I want to carry into every analytical problem I work on.