The COVID-19 pandemic has transformed the workplace and remote work has become an enduring norm. In this episode of Lead with data, Meta’s Arpit Agarwal discusses how the future of work lies in virtual reality, enabling remote collaboration that mirrors in-person experiences. Arpit shares insights from his journey, emphasizing the crucial moments and challenges of analysis in the early stages of product development.
You can listen to this episode of Leading with Data on popular platforms like Spotify, Google Podcastsand Apple. Choose your favorite to enjoy the revealing content!
Key insights from our conversation with Arpit Agarwal
- Future work depends on virtual reality for remote collaboration.
- Launching a data science team fosters innovation and business impact.
- Early product data science prioritizes quality through internal testing and feedback.
- Hiring for data science requires technical skill, problem solving, and strong character.
- Career growth in data science requires broad exploration followed by specialized experience.
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Now, let’s look at the questions Arpit Agarwal answered about his career path and experience in the industry.
How has the COVID-19 pandemic changed the way we work?
The pandemic has fundamentally changed our work dynamics. We have moved from office-centric environments to embracing remote work as a new reality. Even with return-to-office policies, a significant portion of the workforce will continue to operate remotely. The challenge lies in maintaining productivity and fostering the connections that were once built within the walls of the office. Current tools fail to replicate the in-person experience, which is where Meta’s vision comes into play. We are developing products that provide the feeling of working side by side, understanding each other’s body language, and collaborating effectively, all within a virtual space.
Can you share your journey from college to becoming a data science leader?
My journey started at BITS Goa where I pursued a bachelor’s degree in computer science. At first, I focused on academics, but BITS allowed me to explore other interests, including data interpretation. I ran a puzzle club, which sparked my interest in data. After finishing university, I joined Oracle, where I worked in data warehousing and business intelligence, helping clients make data-driven decisions. This experience solidified my interest in analytics and its business applications. I pursued an MBA to deepen my business knowledge and then joined Mu Sigma, where I honed my analytical skills. My career progressed through consulting roles and leadership positions at startups such as Zoomcar and Katabook, where I tackled various data science challenges.
What were the key moments in your career that marked your path?
Joining Zoomcar was a pivotal moment. I was tasked with building the data science team from scratch, allowing me to work on innovative projects such as driver scoring systems using automotive data. This experience gave me the opportunity to work closely with C-level executives and directly influence business decisions. Another significant moment was my time at Katabook, where I helped the company become data-driven and launched several analytics initiatives, including loan offerings based on machine learning models.
Meta’s vision for the future of work revolves around virtual reality, with the goal of creating a space where remote collaboration is as natural and effective as in-person interactions. Data science plays a crucial role in setting ambitious organizational goals for products that are ahead of their time. It involves aligning product strategy with these goals, ensuring product quality, and managing diverse and global teams. Data science also addresses the challenge of analyzing products that are in the early stages of development, where customer data is scarce.
What are the challenges of performing analysis of products that are in the 0 to 1 phase?
Product analysis in the 0 to 1 phase is challenging because there is limited customer data to guide decision making. The focus is on ensuring product quality and functionality, which is critical for enterprise products. We rely on internal testing (dogfooding), alpha and beta testing with select groups, and user research to gather feedback and validate product direction. Once we have a solid foundation, we can launch the product to a broader audience and use data science to measure adoption, retention, and iteration based on user feedback.
<h2 class="wp-block-heading" id="h-how-do-you-assess-candidates-for-data-science-roles-especially-in-emerging-fields-like-generative-ai“>How do you evaluate candidates for data science positions, especially in emerging fields like generative ai?
When hiring for data science positions, I look for candidates with strong problem-solving skills, a deep understanding of the fundamentals of machine learning, and proficiency in programming languages and data manipulation. Specifically for generative ai, candidates should have experience in the relevant domain, such as natural language processing or computer vision. Additionally, I value character and work ethic, which I evaluate through behavioral questions, reference checks, and the candidate’s ability to explain their projects in depth.
What advice would you give to people starting their careers in data science?
For beginners in data science, explore various interests before specializing. Use abundant free learning resources, prioritize skills for value and satisfaction over quick financial gains. Take advantage of opportunities, even in smaller projects or companies, to achieve substantial growth. Recognize that hard work forms the basis of luck; Success is a continuous journey of learning and improvement.
summarizing
Arpit Agarwal’s journey exemplifies the impact of data science on various industries. Meta’s vision for the future of work highlights the critical role data science plays. Aspiring data scientists can gain valuable advice from Arpit’s emphasis on skill development, embracing opportunities, and the lasting journey of continuous learning.
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