Are you excited by the opportunity to pioneer the future of travel? Do you want to redefine mobile travel experiences? Are you ready to fuel industry change and jumpstart your career in Data Science?
Expedia Group paves the way for early career talent to go far, fast with accelerated career advancement, unparalleled access to leadership and a culture of exceptional acceptance.
Travel is so much more than simply reaching your destination. Along your journey, you will make an immediate impact on reimagining the way people search for travel as part of an awesome team that will help invent brand new techniques to bring the world within reach.
From using your strong coding skills to break new ground with machine learning, to applying these new techniques to services that run tens of thousands of requests per second, there is no shortage of opportunities for technical innovation at Expedia Group the sky’s the limit!
Applications to this opportunity are considered for our London offices.
What You’ll do :
You will use apply statistics concepts like confidence intervals, point estimates and sample size to make sound and confident inferences on data and A / B tests.
You will use and manipulate large data sets to design algorithmic and machine learning solutions as well as provide business insights.
Apply solid coding skills, strong analytical and innovative thinking, and Machine Learning expertise to quickly learn new domains and turn innovative ideas into working solutions (from the London Document)
You will also communicate complex analytical topics in a clean & simple way to multiple partners and senior leadership (both internal & external)
Within Data Science, we have three different tracks roles with multiple openings in each of the following areas.
Machine Learning Scientist : Role typically includes building feature pipelines, prototyping new machine learning models and evaluating performance of the algorithms both offline and via A / B tests.
Machine Learning Engineer : Ideal candidate enjoys building ML systems and cares about software engineering principles like CD / CI and code stability, while productionizing machine learning models.
Statistician : Candidates typically have an Operations Research or Statistics background and care about defining measurements for algorithm performance in the wild and help define and implement core e-commerce concepts like customer life-time value, marketing attribution etc.
Who You are :
You are currently pursuing a master's or PhD degree in quantitative fields such as : Computer Science (with focus in areas like Artificial Intelligence, Machine Learning, Natural Language Processing, Data Mining, Data Science), Mathematics ,Statistics ,Operations Research, Electrical & Computer Engineering
Graduating in 2021
You have proven theoretical understanding various machine learning topics like Regression, Naïve Bayes, Decision Trees, Random Forests, SVMs, Neural Networks.
We would like to see experience with statistical computing environments such as R, scikit-learn, SparkML, Python (pandas) etc.
You should have strong knowledge and experience in one or more database technologies, including SQL and other relational databases, no-SQL, and Time Series databases
You understand distributed file systems, scalable datastores, distributed computing and related technologies (Spark, Hadoop, etc.
implementation experience of MapReduce techniques, in-memory data processing, etc.
You have familiarity with cloud computing, AWS specifically, in a distributed computing context.
Computer Languages :
Must-have : Scala and / or Python, SQL
Nice-to-have : Java, R, C++
Data Science Technologies :
A few of these : Spark / PySpark, MLlib, TensorFlow, Keras, PyTorch, Caffe, Python ML libs (Pandas, Matplotlib, Scipy, Sklearn, Numpy etc)
Nice-to-have : Hive, Hadoop, Microsoft SQL Server
Some exciting projects our interns have worked on :
Understanding traveler booking preference is the key to provide efficient matches between travelers and partners. This project leveraged both NLP and ML techniques to extract valuable information from traveler reviews.
By transforming the unstructured data into structured understanding of traveler preferences, it will enable us to enhance listing valuation models, help us identify real competitions among listings and make effective supply acquisition decisions.
Our pricing data science team solves a multi-objective optimization function in the two-sided marketplace to algorithmically identify the best prices to drive more transactions, creating a multi-item trip price incentive for the users and increased transactions for the suppliers.
This is done by creating prediction models for various key performance indices. These predictions come with errors and can result in suboptimal solutions for the optimization.
In this project, we aim to first investigate the Neural network dropout based uncertainty estimation technique and then use it create a robust optimization framework that can solve the multi-objective optimization function more accurately, leading to improved pricing for both the users and the suppliers.
My project on the Data Science team was an incredible experience of being able to dive deeply into embedding algorithms and deep learning architecture, two fascinating areas of data science.
I was also able to work collaboratively with a team of brilliant and supportive data science and machine learning professionals.
Contributing to a project that was challenging and, at the same time, having a direct impact on improving the company’s revenue kept me motivated.
Being surrounded by amazing minds in the team, along with a collaborative learning environment, helped me grow as an emerging data scientist."