Rounak Saha is all about making connections between data, cultures and people. With a Bangladeshi background and a passion for tech and communication, Rounak brings a fresh perspective to the Polaron Multicultural Advisory Panel. Studying data science in Australia has sharpened not just his technical skills but also his ability to bridge worlds through language and empathy. Rounak shared his experiences and strategies for overcoming language challenges and helping people get back on their feet. 

Questions:  

1. How has your participation in the Polaron Multicultural Advisory Panel shaped your understanding of language and communication across cultures?  

Being part of the MAP has been an eye-opening experience for me. Coming from a Bangladeshi background and studying in Australia, I’ve always straddled different cultural worlds—but the MAP gave me a space to truly listen, learn, and share. I’ve come to appreciate that language isn’t just about words—it’s about intent, cultural nuance, and empathy. Communication across cultures requires patience, openness, and a willingness to unlearn assumptions. Through the panel, I’ve learned to approach every conversation with curiosity, not certainty. 

2. In what ways do you think data science can help bridge language or cultural barriers?  

Data science, at its core, is about making sense of complex information—and that includes human behavior, language patterns, and cultural trends. With tools like natural language processing (NLP) and sentiment analysis, we can break down linguistic barriers and better understand how people express themselves across languages and platforms. It can also highlight inequalities in access to services or information, which is key to designing more inclusive systems. When used thoughtfully, data can amplify unheard voices and support culturally sensitive decision-making 

3. Can you share an example where effective (or ineffective) communication significantly impacted a project or group collaboration?  

In one university group project, we were analyzing community health data. While we were technically aligned, our assumptions about the target audience weren’t. I realized late in the process that our presentation used overly academic language that could alienate stakeholders from diverse communities. It was a wake-up call on the importance of not just what we communicate, but how. We ended up reworking our pitch to be more visually intuitive and accessible, which made a huge difference. That experience taught me that communication is as important as analysis. 

4. How do you adapt your communication style when engaging with people from diverse backgrounds or disciplines?  

I try to lead with empathy and active listening. In diverse settings, I avoid jargon unless I’m sure everyone’s on the same page. I also pay attention to non-verbal cues and adapt my tone or pace depending on the group. Whether I’m speaking with someone from a non-technical background or a different culture, I remind myself that communication is about connection, not performance.  

5. How important is language precision in data interpretation and analysis? Have you ever encountered misunderstandings due to unclear data language?  

Precision in language is critical in data science. Mislabeling a variable or miscommunicating findings can completely skew interpretation. I’ve seen cases where “correlation” was confused with “causation” in reports, leading to misguided conclusions. Even small phrases like “statistically significant” can be misunderstood without proper context. As analysts, it’s our responsibility to make our language as clear, accurate, and audience-appropriate as our data. 

6. How do you ensure ethical use of language when interpreting and presenting data insights?  

I ask myself: “Who might be affected by this interpretation? Are we framing this data in a way that’s fair and inclusive?” I try to avoid reinforcing stereotypes or using deficit-based language. For example, instead of saying “low-performing communities,” I might say “communities with limited access to resources.” I also believe in transparency—being upfront about data limitations and assumptions is part of ethical storytelling 

7. How has your ability to communicate technical concepts evolved during your studies?  

Massively! At first, I thought using complex terms showed expertise. But I’ve since learned that simplifying without dumbing down is the real skill. I’ve practiced breaking down algorithms, visualizations, and statistical findings in ways that make sense to people outside my field—whether that’s through analogies, stories, or visuals. Teaching others or working in interdisciplinary teams has really helped sharpen that skill. 

8. What role does language play in your personal and professional development journey?  

Language has been my compass through multicultural transitions – from Bengali childhood to Australian academia. Shifting between educational systems taught me to communicate with precision and cultural awareness. In data science, this means translating complex insights accessibly and ensuring analyses respect diverse perspectives – turning language from a barrier into my strongest bridge 

9. Do you see language as a tool for influence, connection, or problem-solving? Or all three? 

All three, absolutely. Language is how we build trust, solve problems, and inspire action. In the context of data science and multicultural work, it’s the glue that holds everything together. Whether I’m debugging code, sharing insights, or having a heartfelt chat with a teammate—language is always at the center of it.