Hello technology enthusiasts! 

I work as a Sr. Machine Learning Engineer at Expedia Group’s Gurgaon office in India. Having explored and thrived in data and machine learning (ML) for over a decade, I’ve found Expedia Group (EG) to be the perfect place to pursue my intellectual curiosity in this field. I’m delighted to share what makes working at EG a unique and enriching experience. 

Career Journey

My career has been really rewarding so far. It began with a deep-rooted interest in leveraging data to uncover patterns and drive business outcomes. As technology evolved, I saw the potential of machine learning to revolutionize various industries and decided to delve into it. I worked at a couple of companies in this space before Expedia Group. The dynamic nature of this sector, with its vast amounts of data, provided the perfect canvas for applying advanced machine learning techniques. It was this prospect that led me to join Expedia Group. Working in the Gurgaon office, I have had the chance to be at the forefront of innovation, developing solutions and deploying models that enhance traveler experiences, streamlining operations, and contributing significantly to the company’s growth. The rich diversity of opportunities, from recommendation systems to demand forecasting, search and sort, for example, have kept me incredibly passionate about my work.

Day-to-day as a Machine Learning Engineer and Responsibilities

Let’s start with the essentials. The day-to-day activities in my role are a blend of data science, software and data engineering. My role involves harnessing the power of data to create ML solutions tailored for improving travel. My priorities can vary based on the requirements of a specific project, but here’s a general overview: 

Machine Learning at EG

At Expedia Group, machine learning plays an integral role in enhancing and personalizing the travel experience. Here are some quick, tangible, examples but this barely scratches the surface of how ML and AI powers our business, and there are many other powerful applications, too.

The typical workflow of a Machine Learning project in my team

We have a well-defined flow for any machine learning project which incorporates years of experience, optimization and experimentation. This ensures adherence to best practices, reproducibility and consistency in our work across the organization. Once the problem statement is defined, we can define the workflow in the following broad categories: 

EG’s focus on innovative technology 

EG’s commitment to innovative technology that revolutionizes travel experiences truly excites me. A lot of focus goes on personalization and the seamless integration of various platforms to ensure a smooth user journey from start to finish. Additionally, EG’s efforts in promoting sustainable travel through technology demonstrate a forward-thinking and responsible approach. As an engineer, being at the epicenter of these tech-driven transformations in the travel industry is both inspiring and invigorating. 

Life at Expedia Group

Each day I’m immersed in challenges that revolve around enhancing travel experiences for millions worldwide using cutting-edge technology. EG fosters a culture of innovation, pushing me to constantly expand my horizons and adopt new solutions. Collaborating with diverse teams, learning from experts, and seeing our efforts directly impact travelers makes my journey with EG both fulfilling and exhilarating. And not to forget, the travel perks provided by EG are the cherry on the cake! 

Working in Travel 

I appreciate working in the travel sector because it’s about fostering real connections. Every solution we devise directly impacts someone’s journey, whether it’s helping them discover a new destination, connect with different places and cultures, or ensuring their trip goes smoothly. Plus, travel technology inherently generates vast amounts of traveler and product data which is a goldmine for any machine learning professional. I really enjoy the mix of meaningful work, constant learning, and the tangible impact we have on people’s experiences. 

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