From Insights to Impact: Kiran Kumar’s Data Science Career Journey

Data Science, Machine Learning, and Artificial Intelligence are becoming increasingly relevant across industries, including the space sector. From satellite image processing to spacecraft health monitoring, data-driven technologies can support complex operations and help professionals make better use of large and diverse datasets. However, applying Machine Learning to space-related data comes with unique challenges, including limited real-world datasets, complex sensor behaviour, and the need for domain-specific intelligence.

In this interactive DataMites Success Story, Kiran Kumar, a Scientist at ISRO, shares his professional experience in mission operations and discusses how Machine Learning and AI are being explored in the space sector. With 16 years of experience in Bengaluru and involvement in missions including NISAR, Chandrayaan-2, Chandrayaan-3, RISAT, and Cartosat, he explains the opportunities and challenges involved in applying Machine Learning to satellite data.

Insights from Kiran Kumar’s Data Science Learning Journey 

Kiran Kumar shares his Data Science learning experience and explains how Machine Learning is being explored in the space sector. He discusses the techniques he has worked with, the challenges of handling complex satellite data, and the importance of systematic learning. 

Q1. Could you introduce yourself and tell us about your professional background?

My name is Kiran Kumar, and I am working as a Scientist at ISRO. I have been working in Bengaluru for the last 16 years. I completed my B.Tech and have been working in the space sector throughout my Data Science Career.

Q2. What kind of work do you currently handle at ISRO?

I work at the satellite centre, where I am involved in mission operations and analysis. I worked as an Operations Director for NISAR, which is a NASA-ISRO joint collaboration mission. Before that, I worked as an Operations Director for the lander modules of Chandrayaan-2 and Chandrayaan-3. I have also worked on missions such as RISAT and Cartosat.

Q3. How is Machine Learning and AI being used in your field?

Frankly, Machine Learning and AI are still at a preliminary stage in our area of work. I have been working with Machine Learning for the last three years, and we have also given a related project to one of the colleges.The data in our field is very complex. Generic Machine Learning algorithms do not always give 100% results. We need to add other types of intelligence, such as sensor intelligence and sensor behaviour. Otherwise, the direct algorithms do provide results, but they may not be sufficient for our specific requirements.

Q4. Which Machine Learning techniques have you explored in your work?

We initially started applying some probabilistic models along with PCA. We also applied SVM and LSTM. We are getting results in some areas, while in some other areas the results are not clear.Further work is required, which needs a lot of time and different datasets. We also need to synthesise different datasets to progress the work further.

Q5. What are some of the major challenges of applying Machine Learning to satellite-related data?

One of the major challenges is that the real datasets available to us are limited. The data is also very complex and specific to our field.The challenge is to integrate approaches such as LSTM and Transformers with the specific nature of the data. Sensor behaviour and other domain-specific factors also need to be considered. There is a lot of science involved in this area.

Q6. Is Machine Learning already being used in satellite applications?

Yes. Machine Learning is already being used in areas such as image processing for satellites. Applications are still developing, but people are working on them.We cannot ignore Machine Learning now, so we need to equip ourselves with these technologies and understand their data science applications in our field.

Q7. What is your primary role at ISRO? Is it mainly related to data?

My primary work is not mainly related to data. I work in mission operations. We work on spacecraft design and mission operations, including planning the entire mission and satellite operations.We determine requirements such as the orbit, required interfaces and any new software requirements for the control systems. These requirements are given to the designers. Once the design is completed, we work on how the satellite needs to be operated.

Q8. How does satellite data come into your work?

One part of my work involves monitoring the health of the satellite. We receive a lot of data from the satellite, and we need to monitor it for some time to understand the health of the satellite.This is where Machine Learning can be used in my area of work.

Q9. Did you have any prior knowledge of Machine Learning before joining DataMites?

Yes. I had some experience before joining DataMites through self-learning and a project we had worked on. I was aware of Machine Learning before, but I did not have a proper understanding of it.That was one of the reasons I decided to learn it systematically.

Q10. What did you learn through your DataMites training?

After joining DataMites, I learned the basics of Machine Learning and the different models in a systematic manner. Before that, I had some awareness of Machine Learning, but I wanted to understand the concepts and models properly.

Q11. Why did you decide to learn Machine Learning systematically?

I had already been doing some self-learning and had some experience through our project work. However, I wanted to understand Machine Learning in a more systematic manner. That is why I decided to take up data science structured learning and learn the models and basics properly.

Q12. What are your thoughts on the future role of Machine Learning and AI in your field?

Machine Learning is something we cannot ignore anymore. It is already being explored in areas such as satellite image processing. At the same time, further work is required because the data in our field is complex and the available real datasets are limited.The main challenge is to integrate Machine Learning approaches with the specific nature of our data, including sensor behaviour and other domain-specific requirements.

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Key Findings from Kiran Kumar’s Data Science Career Insights 

Kiran Kumar’s experience offers useful takeaways for learners interested in Data Science and Machine Learning. His journey highlights the value of strong fundamentals, practical learning, and domain knowledge.

• Machine Learning is emerging in the space sector, but its use is still at a preliminary stage in some areas.
• Satellite data is highly complex, making it difficult to rely only on generic Machine Learning algorithms.
• Sensor intelligence and sensor behaviour need to be considered when applying Machine Learning to satellite-related problems.
• Limited real-world datasets are one of the major challenges in developing and testing Machine Learning applications.
• Techniques such as PCA, SVM, LSTM, probabilistic models, and Transformers are being explored for different applications.
• Machine Learning is already being used in satellite image processing, while other applications continue to develop.
• Satellite health monitoring is an area where Machine Learning can potentially support mission operations by analysing satellite data over time.
• Applying Machine Learning effectively requires a combination of technical knowledge and domain-specific understanding.
• Systematic learning can help professionals develop a clearer understanding of Machine Learning concepts and models beyond self-learning and project experience.
• The future of AI and Machine Learning in the space sector will depend on further research, better datasets, and the ability to adapt models to specific mission and sensor requirements.

Refer these articles : 

• How Varun Built a Successful Career in Data Science
• Devadarsan’s Journey from Fresher to Data Scientist
• How Rakshitha Successfully Transitioned Into Data Science

The discussion with Kiran Kumar highlights the growing role of Machine Learning in the space sector and the importance of combining technical skills with domain knowledge. His experience shows how structured learning can strengthen understanding of Machine Learning concepts and their practical applications. Exploring IT courses in Hyderabad  can also help learners build relevant skills for emerging technology careers. Kiran Kumar’s journey demonstrates how structured learning and consistent skill development can help professionals build a stronger understanding of Machine Learning and explore its applications in their respective fields. 

Kiran Kumar’s journey from having some prior knowledge of Machine Learning through self-learning and project experience to developing a more systematic understanding through DataMites highlights the value of structured learning. Through training, he strengthened his understanding of Machine Learning concepts and models and explored how these technologies can be applied to complex satellite data and mission operations. According to Grand View Research, the global data science platform market was valued at USD 96.25 billion in 2023 and is projected to reach USD 470.92 billion by 2030, growing at a CAGR of 26.0% from 2024 to 2030. If you’re exploring a Data Science institute in Hyderabad, structured learning can help you develop practical skills and build a stronger foundation in the growing data industry.

Whether you’re a professional looking to strengthen your Machine Learning knowledge or an aspiring data professional like Kiran Kumar, DataMites Institute offers structured learning to help develop practical, industry-relevant skills. With 50+ offline centres and online learning options, DataMites provides Data Science courses in Delhi and training opportunities in Chennai, Hyderabad, Pune, Mumbai, Coimbatore, Ahmedabad, Kolkata, Noida, Indore, Jaipur, Chandigarh, Kochi, Nagpur, and Bhubaneswar. DataMites has also received the Best Skill Development EdTech award, recognising its contribution to skill development and career-focused learning. Through hands-on projects, expert mentorship, internship opportunities, interview preparation, and IABAC® and NASSCOM FutureSkills® certifications, learners can strengthen their technical knowledge and prepare for opportunities in the data industry.