Vijayasarathi Balasubramanian, Senior Data and Applied Science Architect, Microsoft

Vijayasarathi Balasubramanian, known as Vijay, works for Microsoft as a Senior Data and Applied Scientist. Vijay obtained his master’s in data science from the University of Notre Dame, and he has 17 years of progressive professional experience in technology and its practical applications. Vijay is associated with prestigious associations, including IEEE, IET, and BCS. Indian Achievers Forum awarded Vijay the “International Achievers Award,” and Golden Bridge (Globee) awarded the Data Scientist of the Year award. Vijay frequently discusses technology and its applications in various forums, especially Human Resources.

Recently, in an exclusive interview with Digital First Magazine, Vijayasarathi shared his insights on the importance of data science and business analytics in today’s business landscape, his professional journey, current roles and responsibilities at Microsoft, significant career milestones, words of wisdom, and much more. The following excerpts are taken from the interview.

In your opinion, why are data science and business analytics more important today than ever before?

In my view, data science has emerged as a crucial differentiator in the contemporary business landscape for a multitude of reasons. Foremost, we must acknowledge the technological advancements that have enabled us to store and process vast volumes of data. The advent of sophisticated analytical tools, machine learning algorithms, and enhanced computational capabilities has simplified and economized the analysis of extensive datasets. Additionally, the digital era, marked by the rise of the internet, social media, and the Internet of Things (IoT), has led to an unprecedented surge in data generation. This “Big Data” is a treasure trove of insights waiting to be harnessed. Furthermore, businesses that adeptly analyze this data can carve out a competitive advantage, allowing them to make insightful decisions, forecast trends, and deeply understand their clientele. To encapsulate, the significance of data science in today’s world is anchored in data’s vast potential to catalyze business growth, innovation, and efficiency. As data’s magnitude and diversity burgeon and technology perpetually advances, data science’s influence on shaping businesses’ future is bound to amplify.

What can leaders do to promote a data driven decision-making culture in their organizations?

Promoting a data-driven decision-making culture within an organization is a multifaceted endeavor that hinges on strategic initiatives, technological investments, talent management, and a transformative mindset. Leaders play a pivotal role in this transition by leading through example. Their commitment to decisions anchored in data and analytics sets the tone for the entire organization. By consistently showcasing the utilization of data insights in various forums, from team to town hall meetings, they underline its paramount importance. It’s equally vital to invest in robust infrastructure, encompassing data warehouses and cutting-edge analytics software, while staying attuned to emerging technological advancements. Talent management is another cornerstone; offering training and upskilling opportunities in data analytics underscores the role of data in decision-making. Concurrently, hiring data experts, from scientists to analysts, accelerates the organization’s data-driven journey. A thriving data culture also thrives on curiosity, clear definitions of objectives, open communication, and rewarding data-driven achievements. Ethical data usage, compliant with regulations, ensures the sustainability of these initiatives. By championing these principles and continually spotlighting data’s significance, leaders can effectively embed a data-driven ethos within their organizations.

Vijay, can you brief us about your journey into technology and what drew you into the data science space?

From my undergraduate days at Thiagarajar College, I was captivated by technology’s potential. My early achievements, like the graphical program for electrical machines, hinted at my future in data. At British Telecom, I ventured into data optimization, establishing innovative systems. My role at The Home Depot deepened my data management skills, but it was at AT&T where my passion for data science truly ignited. Here, I employed machine learning to decipher customer behaviors. Now at Microsoft, I’m leveraging data science for personalized customer experiences. My journey, from engineering to data science, reflects my drive to transform raw data into impactful insights.

Please share a brief introduction about your role as Senior Data and Applied Science Architect at Microsoft.

As a Senior Data and Applied Science Architect, especially within the e-commerce domain, I harness the power of data to drive business decisions and enhance customer experiences. I design and implement advanced data models and algorithms, ensuring the seamless integration of vast datasets from various sources. Leveraging machine learning and AI, I optimize e-commerce platforms for personalized user experiences, product recommendations, and efficient transaction processes. My expertise aids in predicting market trends, understanding customer behaviors, and ensuring the platform’s scalability and performance. In essence, I focus on data strategy, ensuring data-driven excellence in every user interaction.

With 17+ years of industry experience, according to you, what skills or characteristics make someone a seasoned data scientist?

Becoming a seasoned data scientist requires a multifaceted skill set. On the technical front, a strong foundation in programming, machine learning algorithms, and statistics is crucial. Familiarity with tools such as Hadoop, Spark, and others is essential for managing large datasets, and one cannot overlook the importance of data visualization and database management. In terms of domain knowledge, a deep understanding of the specific industry’s nuances, challenges, and objectives is vital. This is complemented by data intuition, which is the ability to form educated guesses and hypotheses based on data trends. Soft skills also play a significant role; a seasoned data scientist possesses robust problem-solving capabilities, excels in communicating complex data insights to non-technical audiences, and collaborates effectively with colleagues from various disciplines. In summary, while technical expertise is a cornerstone, it’s the combination of soft skills, business acumen, and a commitment to continuous learning that distinguishes a seasoned data scientist.

What types of career opportunities are available in the field of data science? Could you please share some insights?

Certainly! Data science is a vast domain, teeming with diverse career opportunities. As data-driven decision-making becomes integral to organizations, there’s a burgeoning demand for experts in this field. While roles can differ based on organizational size, a few standout positions include Data Scientists, who delve into large datasets to extract actionable insights, craft predictive models, and tackle intricate business challenges, armed with skills in machine learning, statistics, and programming languages like Python and R. Data Analysts focus on interpreting data, crafting reports, and utilizing visualizations to facilitate business decisions, often leveraging tools like SQL, Tableau, and Power BI. Data Engineers, pivotal to the data science process, architect, and maintain large-scale systems, ensuring data’s cleanliness and accessibility, with proficiency in big data technologies like Hadoop and Spark, as well as various database systems. The field also boasts specialized roles such as Machine Learning Engineers, Statisticians, BI Analysts, Quantitative Researchers, Data Visualization Specialists, and Big Data Architects. In essence, data science presents a rich tapestry of career avenues, each offering unique challenges and rewards. Success hinges on aligning with a role that resonates with one’s passion and fortifying it with continuous learning in this ever-evolving domain.

What has been your most career-defining moment that you are proud of?

When asked about a pivotal moment in my career that I take immense pride in, I immediately reflect on a period during my mid-career. It was then that I was presented with the chance to develop what I fondly term as a “next-generation data product.” This product was designed with a singular vision: to address intricate enterprise business challenges that would significantly influence business outcomes.

What are your passions outside of work?

Outside of my professional life, I’m deeply passionate about practicing yoga, which serves as a conduit for both my mental and physical well-being. Additionally, I indulge in writing blogs, offering a platform to express and share my thoughts and experiences with a wider audience.

What is the best career advice you’ve received and how have you sought to put this into practice?

Early in my career, my mentor imparted invaluable career advice that has stayed with me. He emphasized the importance of continuous learning, highlighting that in our ever-changing world, it’s crucial to stay updated with emerging technologies and methodologies. He suggested enrolling in courses on platforms like Coursera, Udemy, and LinkedIn Learning, dedicating time to reading industry-related materials, attending seminars and conferences, and consistently seeking feedback to understand areas for growth. He also stressed the significance of adaptability, noting that change is inevitable. To navigate this, he recommended developing a diverse skill set, remaining open-minded to new approaches, and adopting a problem-solving mindset. Lastly, he underscored the essence of building genuine relationships. Beyond just networking, it’s about establishing sincere, mutually beneficial connections. This involves attending industry events, seeking mentorship, staying in touch with contacts, and practicing active listening to foster trust and deeper relationships.

What is your biggest goal? Where do you see yourself in 5 years from now?

In terms of my overarching ambition, I aspire to found a state-of-the-art Research Institute that delves into advanced data analytics, machine learning, and invention science. I envision this institute as a beacon of innovation, spearheading pioneering research and acting as a nexus for technological breakthroughs. Integral to this dream is the collaboration with esteemed academic entities and industry stalwarts, amplifying the institute’s influence manifold. Projecting five years into the future, I see myself steering this Research Institute, guiding a cadre of adept researchers and experts in redefining the realms of data analytics and machine learning. By then, the institute might have garnered global acclaim for its seminal contributions. Furthermore, I anticipate having cemented pivotal alliances with academic and industry leaders, synergizing our collective expertise on transformative ventures. In sum, half a decade from now, I aim to have left an indelible mark on the global community through my endeavors.

What advice would you offer others looking to build their careers as data scientists?

Embarking on a career in data science is a thrilling yet demanding endeavor, especially considering the field’s dynamic landscape. For those venturing into this domain, it’s imperative to solidify foundational knowledge. This includes mastering mathematical concepts like linear algebra, calculus, and statistics, and gaining proficiency in programming languages, notably Python or R. While a broad understanding is commendable, carving a niche in areas like deep learning or natural language processing can set you apart. Gaining hands-on experience is equally vital. Engaging in real-world projects, whether through internships or personal endeavors, bridges the gap between theory and practice. Platforms like Kaggle offer a space to refine skills and earn accolades. Given the ever-evolving nature of data science, staying updated is non-negotiable. This involves perusing research articles, attending seminars, and participating in workshops. A well-curated portfolio on platforms like GitHub can be a testament to your expertise, offering tangible proof to prospective employers. Networking, too, plays a pivotal role. Engaging with peers on platforms like LinkedIn, attending conferences, and being active in data science communities fosters learning and collaboration. Soft skills, often overlooked, are paramount. Effective communication, a knack for problem-solving, and adeptness at teamwork are essential. Moreover, being ethically conscious, especially when handling sensitive data, is crucial. Augmenting your data science acumen with business insights enhances the relevance of your analyses. Diversifying skills, by delving into areas like data engineering or MLOps, amplifies your versatility. Ultimately, the journey to becoming a distinguished data scientist is ongoing, characterized by perpetual learning, innate curiosity, and a fervor for gleaning insights from data.

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