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Introduction
Do you want to become a Machine Learning Engineer at Expedia❓ but are confused about the preparation strategies and resources to follow? So, don't worry, here we will discuss the roles and responsibilities of a Machine Learning Engineer at Expedia, its salary and other benefits, job expectations, and the resources to prepare for the same.
Before discussing other essential things related to the job, let's have an introduction to Expedia.
About Expedia
Expedia is an American online travel and shopping company for small business travel and other customers. Rick Barton founded Expedia Group in October 1996. Expedia powers travel everywhere and to everyone through the global platform, hotel reservations, airline bookings, and many beneficial vacation packages. It has over one lakh partners worldwide to meet every budget and service.
Expedia has transferred the power of booking to the consumers and has become the world's largest full-service travel website.
Expedia has made its app interface so friendly that you need a few taps to select a hotel, flight, car, or other services.
There exist around 24 million active reward members of Expedia across 32 countries.
Expedia has more than 30 sites in more than 18 languages, making it accessible globally in your own language.
Expedia has created an inclusive work environment with a divergent workforce. All the applicants with the desired qualifications will get a chance to receive consideration for employment irrespective of race, gender, religion, national origin, sexual orientation, disability, or age.
They have partnerships with more than 500 airlines around the globe.
Expedia mobile apps and websites offer travelers the tools to search and book everything from flights and hotels to rental cars, vacations, and other local activities with up-to-the-minute trip alerts and details in a radically efficient and simple way, depending on the device you are using at the time.
About the Role🧑💻
Machine Learning Engineers are expected to focus on research and create AI(artificial intelligence) algorithms that can be used to develop AI systems to automate predictive models. They are expected to work with a data science team and stay in touch with data analysts, engineers, architects, and scientists of their team and other teams like software developers and web developers. A few responsibilities of this role includes:
Selecting a significant data set
Running ML(Machine Learning) tests on the data
Designing machine learning and artificial intelligence systems
Improving models by using previous results
Extend ML libraries
Skills Required
Minimum Qualifications
🎓 Bachelor's or Master's or Ph.D. degree in an analytical-related or technical subject such as Computer Science (with focus on areas like Artificial Intelligence, Natural Language Processing, Machine Learning, Data Science, Data Mining, Statistics, Mathematics, Operations Research, Physics, Electrical & Computer Engineering.
Required Experience
📌 Skilled in efficiently using python, and demonstrated strength with various other programming languages.
📌 Familiar with more than one API(Application Programmable Interface) access or data store platform and Big Data applications such as Hive, PostgreSQL, ElasticSearch, DataBricks, Tableau, Dremio, etc.
📌 Ability of testing and monitoring your own code and understanding different models training approaches, optimization approaches, using parameters, etc.
📝 Knowledge of one or more database technologies, including SQL (Structured Query Language) and other relational databases, time series databases, and no-SQL.
📝 Understanding distributed file systems, distributed computing, scalable datastores, and related technologies like spark, Hadoop, etc.)
📝 Experience working with technologies like MapReduce, in-memory data processing, etc.
The salary figures mentioned above are subject to change.😐
Key Responsibilities
The primary responsibility of a Machine Learning Engineer is to research and create artificial intelligence algorithms for creating AI-based systems.
A few responsibilities that a Machine Learning Engineer at Expedia has to work upon are:
Work in a cross-functional team of data and machine learning engineers to design and code large-scale batch and real-time data pipelines in AWS(Amazon Web Services).
Make recommendations for machine learning/software design patterns and leveraging emerging technologies.
Meet and collaborate with peers across environments to solve problems.
You will seek knowledge from subject matter experts when needed.
Understand the importance of technology and system integration and the basic features and facilities involved in the integration process.
Apply software design principles, data structures and design patterns, and computer science fundamentals to write clean, maintainable, optimized, and modular code with good naming conventions.
Use knowledge of database design to solve data requirements.
Develop data applications using distributed ETL(Extract, Transform and Load) frameworks.
Coordinate with inputs of stakeholders when developing solutions to issues.
Execute tasks and provide data to support the implementation of holistic solutions that forge linkages between structure, people, process, and technology.
Career Roadmap
To begin and advance your career as a Machine Learning Engineer, you need to follow the below-given steps-
Pursue a Degree
You should pursue a bachelor's or master's degree before concentrating solely on your chosen industry in technical or analytical-related fields like:
Electrical Engineering: This field of study is focused on the engineering discipline concerned with the design, study, and application of equipment, devices, and systems that use electricity, electronics, and electromagnetism.
Computer Engineering: To prepare the graduate for developing and using technologies, as well as being able to design, produce, and manage data elaboration systems in a wide range of applications.
Mathematics/ Statistics: To prepare students to design and implement solutions to practical problems in science, engineering, and many other fields, offering two areas of emphasis.
Data Modelling
Analyzing unstructured data models is one of a machine learning career's key responsibilities, that brings us to one more skill required which is data modeling. Having a good knowledge of data modeling is essential for every student who wants to pursue a career in machine learning. They are expected to identify patterns, analyze data structures, and evaluate the data with the help of the most suitable algorithm such as a clustering algorithm, classification algorithm, etc.
Machine Learning Algorithms
A Machine Learning Engineer must grasp the commonly-used machine learning algorithms and know how to use them efficiently. Supervised, reinforcement, and unsupervised machine learning algorithms are the three most frequently used machine learning algorithms which are further divided into- K Means Clustering, Naive Bayes Classifier, Logistic Regression, Linear Regression, Random Forests, Decision Trees, etc. So, if you're interested to start your career in machine learning, you should have a solid understanding of these algorithms.
Projects
After acquiring theoretical knowledge and developing the skillset essential for pursuing a career in machine learning, you should start working on some beginner-friendly projects because hands-on experience is essential. Here is the list of some projects you can start with:
Chatbots: Chatbots are great projects to get involved with. One can build chatbots similar to the ones integrated into many of the websites and applications we use today. Deep learning and Artificial Neural Networks can be used to develop advanced chatbots capable of answering complex questions and participating in complex conversations.
Music Recommendation System: Budding AI developers can create music recommendation systems built upon music and genre datasets. Complex recommendation systems can be built with massive datasets or open libraries and neural networks.
Sentiment and Behaviour Prediction: AI can be used for the identification of behavioral patterns and sentiments of targets. This enables sentiment or behavior prediction through the use of AI.
Market and Stock Prediction: A good idea for artificial intelligence projects is market and stock prediction engines. This takes the data from the market into account using advanced analytics to determine the outcome of stocks or finance.
Chess and Other Games: Developing chess with advanced AI which can act as NPC (Non-player character) is an awesome way to indulge yourself in artificial intelligence projects. This type of project is fun, and the AI developer determines the AI opponent's level of difficulty.
Preparation Tips and Resources💡
To prepare, we must have a collection of good resources. We have already created a full-fledged course on machine learning; here is the video to introduce the machine learning course.
You can search for many resources, but we always need a systematic way to learn things. Here are some important resources which help to become a Machine Learning Engineer at Expedia:
As Machine Learning Engineers are in regular contact with development teams so having knowledge Web technologies and Mobile technologies will be considered a plus point.
Also, prepare yourself with attractive answers to clear the behavioral test round:
Tell me about yourself
Explain a most recent project
State your weakness and strengths
Why are you interested in joining Expedia?
Justify that you are a good team player
While giving your interview, just be relaxed and give answers calmly and confidently.
We wish you all the very best.🤗
Frequently Asked Questions
Who is a Machine Learning Engineer?
Machine Learning Engineers are expected to focus on research and create AI(artificial intelligence) algorithms that can be used to develop AI systems to automate predictive models.
What is the hardest part about being an ML engineer?
There exist few factors that make machine learning difficulties that are: it requires in-depth knowledge of aspects of computer science and mathematics and also attention to detail one must take in identifying inefficiencies in the algorithm.
Whom does Machine Learning Engineer need to collaborate with?
Machine Learning engineer typically works with a larger data science team and are required to communicate with data scientists, data analysts, administrators, data engineers, and data architects.
Is Machine Learning Engineer a good career option?
Yes, machine learning can be considered a good career path because it is one of the top jobs in the United States in terms of growth of postings, salary, and general demand.
Who can become Machine Learning Engineer?
A candidate has a master's degree and sometimes a Ph. D. holder in computer science or other related fields. Also, advanced data analytical and mathematics knowledge are critical components of a machine learning engineer's background.
Conclusion
In this article, we have widely discussed the details of becoming a Machine Learning Engineer at Expedia. We have explored the salary and perks, skills required, roles, and responsibilities of a Machine Learning Engineer at Expedia and discussed the preparation strategy to prepare yourself for securing this position. Also, refer to our below articles on Expedia:
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