Texas A&M Institute of Data Science: Two graduates share their master’s journey

If you’re looking to study data science abroad, you’ll want a programme that prepares you for the industry while helping you build connections in a new country. At the Texas A&M University Institute of Data Science (TAMIDS), the Master of Science in Data Science (MSDS) combines interdisciplinary learning with opportunities to connect with students from different academic and professional backgrounds.

Through the programme, you’ll explore how data science is applied to real-world challenges while connecting with researchers and industry professionals who can support your academic and career goals.

Outside the classroom, you can take part in TAMIDS’s annual Data Challenge or get involved with student organisations. You can also explore research through Data2Reality, where you can gain experience working on data-driven projects. And through the Industry Alliance Network, you can connect with industry partners through mentorship opportunities and project-based learning. Beyond TAMIDS, Texas A&M offers career resources such as one-to-one career advising, workshops and HireAggies, where you can explore internship and job opportunities. Together, these experiences help you put your skills into practice while building connections that can support your career.

But what can you really expect from studying data science at TAMIDS? We spoke to two MSDS graduates to give you a closer look.

Their experiences show how the programme can support different academic and career paths. Asmita Shivling Desai completed a research assistantship at TAMIDS during her final year, focusing on applied machine learning (ML) and data engineering. Rahul Gautam received a Graduate Research Assistantship through TAMIDS in partnership with Tenaris, a leading supplier of steel pipes and related services for the world’s energy industry. After graduating, he joined Visa as a Senior Machine Learning Engineer.

Asmita Shivling Desai, MS Data Science

Can you tell us more about your specialisation and past and present projects related to the MSDS programme?

The Departments of Computer Science and Engineering (CSE), Electrical and Computer Engineering (ECEN), Mathematics (MATH), and Statistics (STAT) collaborate with TAMIDS to offer the MSDS. Each department runs a track in the programme.

My specialisation has been in applied machine learning, data engineering, and, lately, AI and LLM integration. I got there through a research assistantship with TAMIDS. There, I used Amazon Web Services to take the Research Affiliate project from raw data to a working production platform, then continued building on it with further enhancements.

Outside of that, I built Blaze Defender, a wildfire risk dashboard in React and Node.js covering exploratory data analysis, predictive modelling, and decision support. I also presented on the LOGIN paper, an LLM-consulted Graph Neural Network (GNN) training framework at the Web Search and Data Mining (WSDM) 2025 conference. Those projects are where the degree stopped being theory and started becoming real pipelines, real models, and real deployment, which is essentially how I still approach my work today.

What kind of research and projects can students expect to work on during their time at Texas A&M?

I did a research assistantship at TAMIDS during my final year in the programme. My job there was to use AWS to take the Research Affiliate project’s data from an early stage to a real production platform, then improve it as I went. It tied directly to my coursework since I was applying the same modelling and analysis skills to something that actually had to run and hold up, rather than just a class assignment. Most of my hands-on experience, though, still comes from my five-plus years in industry data engineering roles before and during the programme, and that’s really what shaped how I think about the technical side of the degree.

Is studying data science worth it?

Yes, it’s been worth it for me. Going in, I already had five-plus years as a data analyst and engineer at places like Tata Consultancy Services, JP Morgan, and NTT Data, but the degree gave me a real, structured foundation in machine learning and AI that I hadn’t picked up on the job. It’s opened doors into AI engineering specifically, things like Retrieval-Augmented Generation (RAG) pipelines and LLM integrations with AWS Bedrock and OpenAI, that weren’t part of my earlier roles at all. So, for me, it wasn’t about starting a career from zero, it was about unlocking a track I couldn’t have moved into on experience alone.

The co-curricular opportunities were valuable too. During my final year, I worked as a research assistant at TAMIDS on its AI-integrated scholar portal. I worked on the React and Node.js front end, which handles the user-facing side of the portal, as well as AWS pipelines that manage data and cloud processes. I also worked with LLM-generated summaries, using AI to turn research information into concise, useful summaries at scale. This gave me hands-on experience building and scaling AI applications beyond the classroom.

Should I pursue data science in 2027, or is the field at risk because of AI?

Yes, I’d still say pursue it, but go in expecting the job itself to keep changing. AI has already changed how I work day to day. Tasks like data pipeline design and dimensional modelling get automated in pieces, and what matters more now is judgment, knowing how to architect something and where to plug in AI responsibly instead of doing everything by hand.

My degree gave me the statistics and engineering fundamentals that AI tools speed up but don’t replace, so I didn’t feel like the ground shifted under me. Honestly, the real risk isn’t AI killing the field, it’s finishing the degree without also building fluency in the AI tools that now sit on top of it.

Should I pursue a data science degree or a computer science degree?

A data science degree gets you into applied statistics, machine learning, and data-driven decision making faster and more directly than a general computer science degree does. Computer science trains you broadly across software systems and theory, while data science is built specifically around extracting insight and building models, which is what most analyst, data engineer, and AI engineer roles actually need day to day.

If your goal is building pipelines, running experiments, or working on machine learning and AI systems, data science is the closer fit. If you want broader software engineering or systems work, computer science probably serves you better. It really comes down to which side of the work you want to be doing.

What is the return on investment (ROI) for data science degrees?

It depends on what you do with the degree. My own ROI has been solid because I paired it with specific, in-demand skills, cloud computing, Snowflake, Matillion, and now AI and LLM tooling like RAG and Bedrock, instead of treating the degree itself as the thing that does the work. It’s also what lets me move from Data Engineer into Analytics Engineer and AI Engineer territory, which I don’t think I’d have reached based on my prior experience alone. But if you finish the programme without building comparable hands-on skills, I get why the ROI would look thin. The degree isn’t the guarantee, what you build on top of it is.

What inspired you to become a data scientist?

It goes back to my childhood in Solapur, watching my father make farming predictions based on patterns in weather, soil, and past yields. Seeing him work with uncertain, real-world data to make decisions that directly affected our family stayed with me long before I had a name for it. That instinct resurfaced during my years as a data engineer, where I kept seeing the same idea play out at enterprise scale: using patterns in data to predict outcomes and guide real decisions. Pursuing an MS in Data Science was really just me putting a formal structure around something I’d been curious about my whole life.

Why did you choose data science as a career?

I chose data science because it let me put a name and a skill set to something I’d been drawn to since I was a kid, watching my father predict farming outcomes. Day to day, that means building and maintaining data pipelines, developing RAG- and LLM-powered applications, and increasingly working with agentic AI frameworks to solve real problems for stakeholders.

It’s not glamorous most days; a lot of it involves debugging pipelines, tuning queries, and translating messy requirements into something a model can actually use. But I don’t regret it. I like that the field keeps moving, and I’ve been able to grow from traditional ETL work into AI engineering without having to start over. That’s kept it interesting instead of stale.

Does a glass ceiling exist for specialist roles such as data scientists?

I’ve heard the glass ceiling concern before, and I think it’s fair but not inevitable. At some point, career progression means making an active choice, going deeper into a senior technical or staff-level track, moving into AI-focused or applied research work, or pivoting into product or leadership roles that use the same data instincts differently.

From my own path across Data Engineer, Analytics Engineer, and now AI Engineer, the ceiling has more to do with staying stuck on one tool or platform than with the data scientist title itself. Staying adaptable across data engineering, analytics, and AI is really what keeps the path open.

What’s next after becoming a Data Scientist?

What’s next for me is going deeper into AI engineering while staying grounded in data engineering, which feels like the natural next step from the degree. Right now, I’m working as a Data Engineer at a startup, building on five-plus years of data analyst and engineer experience at NTT Data, JP Morgan, and Tata Consultancy Services before that.

I’m actively building out my AI engineering side too, working toward the AWS Certified AI Practitioner certification and Anthropic’s Building with the Claude API course, alongside hands-on RAG and LLM work with AWS Bedrock and OpenAI. The degree gave me the modelling and statistical grounding to make that move credibly, instead of just bolting AI tools onto a workflow I didn’t fully understand.

Rahul Gautam, MS Data Science (Language Intelligence specialisation) graduate with perfect GPA

Can you tell us more about your specialisation and past and present projects related to the MSDS programme?

I completed the MSDS programme under the Computer Science and Engineering track, where my academic focus centred on the application and implementation of advanced machine learning and data science algorithms. During my studies, I was awarded a Graduate Research Assistantship through TAMIDS in partnership with Tenaris. This opportunity allowed me to contribute to industry-driven research aimed at solving real-world problems, further strengthening my technical and analytical capabilities.

Prior to joining the programme, I worked as an AI Engineer, collaborating with R&D teams to design and develop both tabular and computer vision models for smart appliances. My role involved building intelligent systems to enhance automation and reduce manual intervention. This professional experience aligned closely with the MSDS curriculum, which offered a strong foundation in core data science principles as well as advanced coursework that deepened my expertise and broadened my ability to deliver impactful, data-driven solutions.

What kind of research and projects can students expect to work on during their time at Texas A&M?

I gained substantial hands-on experience through my research work with Tenaris as part of my Graduate Research Assistantship. In this role, I developed machine learning algorithms to predict yield strength using chemical composition and process parameters. My responsibilities included performing extensive feature engineering to derive meaningful variables, evaluating multiple modelling approaches to identify the best-performing model, and applying explainable AI techniques to interpret model behaviour and determine which parameters most significantly influenced the target yield strength.

This project was closely aligned with the MSDS curriculum, as it required applying core concepts from machine learning, statistical modelling, and data engineering to address a real-world industrial problem.

Is studying data science worth it?

Yes, studying data science is still absolutely worth it. My MSDS degree has directly supported my career growth, from my research work with Tenaris to my current role as a Senior Machine Learning Engineer at Visa. The programme gave me a strong foundation in machine learning, statistical modelling, and data engineering, skills I rely on daily while building in-house AutoML platforms that automate feature engineering, model development, and model serving.

The degree opened doors to opportunities across both research and industry, and it continues to provide long-term value by keeping me adaptable in a rapidly evolving field.

Should I pursue data science in 2027, or is the field at risk because of AI?

You should still pursue data science in 2027. AI has changed the field, but it hasn’t replaced it.  Rather, it has expanded it. In my current role at Visa, AI has actually made my work more impactful. I build internal AutoML systems that streamline the entire model lifecycle, but these tools still rely heavily on core data science principles such as feature engineering, model evaluation, and interpretability.

My data science degree has helped me adapt quickly to new AI technologies. The fundamentals in statistics, machine learning theory, data processing, and critical thinking remain essential even as automation increases. AI is not a threat to data science; it’s a catalyst. People who understand both AI and data science will be in even higher demand.

My experience at Texas A&M also extended beyond the classroom. The university offers a wide range of student organisations and activities, from the Graduate and Professional Student Government and Student Government Association to recreational sports and university events. Getting involved in student organisations and activities gave me opportunities to connect with people from different backgrounds and collaborate outside my academic work. These experiences complemented my technical training and contributed to a well-rounded graduate experience, helping me grow both as a data scientist and as a person.

Should I pursue a data science degree or a computer science degree?

A data science degree offers advantages for individuals who want to work directly with data, modelling, and applied machine learning. While computer science provides a broad foundation in algorithms and systems, data science focuses on extracting insights, building predictive models, and solving real-world problems using data.

My own career reflects this distinction. The MSDS programme prepared me for roles where modelling, experimentation, and statistical reasoning are central, from developing yield-strength prediction models at Tenaris to building AutoML platforms at Visa. For students who want to work at the intersection of data, AI, and decision-making, a data science degree is often the better fit.

What is the return on investment (ROI) for data science degrees?

Salary is only one part of the ROI equation. The long-term value comes from the versatility of the skills and the wide range of industries that rely on data-driven decision-making.

My degree has already led to strong career progression from AI engineering to research roles and now to a senior machine learning engineering position at Visa. The skills I gained are transferable, future-proof, and applicable across domains such as finance, manufacturing, healthcare, and technology. From a long-term perspective, the ROI is strong because the field continues to grow and evolve.

What inspired you to become a data scientist?

My inspiration came from seeing how data and machine learning could solve meaningful problems. Early in my career, I worked with R&D teams building AI models for smart appliances, and I saw how intelligent systems could automate processes and improve efficiency.

That experience sparked my interest in understanding data more deeply and not just building models, but learning how to engineer features, evaluate algorithms, and interpret results. The MSDS programme strengthened that passion, and my later work with Tenaris and Visa confirmed that this was the right path for me.

Why did you choose data science as a career?

I chose data science because I enjoy the combination of analytical thinking, creativity, and problem-solving that the field requires. My daily work now involves designing and building AutoML systems that automate the entire model lifecycle from feature engineering to model serving.

There are challenges, such as handling complex datasets or optimising models for production, but the rewards outweigh them. Seeing a system I built reduce model development time or improve automation is extremely fulfilling.

I don’t regret choosing this career. It has met my expectations by offering continuous learning, meaningful work, and opportunities to grow technically and strategically.

What does the career trajectory look like for an MSDS graduate?

The field offers multiple paths for advancement. You can grow into senior technical roles, principal engineering positions, or specialised AI roles. You can also move into leadership positions such as Data Science Manager, AI Lead, or Head of Machine Learning Platforms.

My own progression from AI Engineer to Graduate Research Assistant to Senior Machine Learning Engineer at Visa reflects the flexibility and upward mobility available in this field.

What’s next after becoming a Data Scientist?

After becoming a Data Scientist, your career can evolve in several directions. In my case, I transitioned into more engineering-focused roles, eventually becoming a Senior Machine Learning Engineer at Visa. Today, I work on building internal AutoML platforms that improve automation and reduce model lifecycle time.

Many data scientists move into senior technical roles, machine learning engineering, AI research, or leadership positions. Others transition into product or strategy roles.

The MSDS degree provides a strong foundation that supports long-term growth, whether you choose to deepen your technical expertise or expand into broader decision-making roles.

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