Travel platform pricing and monetization has critical impacts on the business health. The Machine Learning Science team is building a scalable and robust system to enable pricing as a lever to optimize business performance. The system should be steered based on desired outputs, such as a given tradeoff between volume and profit. Our work directly decides prices across different lines of business, influencing multi-billion dollar revenue.

As a Machine Learning Scientist III, you will research and build this pricing system. You will work on end-to-end pricing problems. You will collaborate with a strong team of machine learning scientists, data scientists, engineers, product managers, and operation analytics. You will contribute to an area of active scientific research: causal inference using machine learning methods, operations research and optimization, ML-based demand estimation, experiment design with spillovers. And this is an applied research role - your models will be deployed to our production systems, and your results will be measured objectively through AB testing.

In this role you will:

• Apply solid scientific skills, strong analytical and innovative thinking to quickly learn new domains and turn innovative ideas into working solutions

• Articulate technical solutions and plans to stakeholders based on detailed understanding of business requirements.

• Research and implement scalable machine learning and data science solutions end to end with engineering rigor.

• Follow the latest technology and research and be able to customize them to our problem space.

• Design success metrics for both short-term and long-term performance.

• Guide solutions using data insights based on deep understanding of business domains.

• Collaborate with other machine learning and data science teams to build a great data science culture in Expedia Group.

Experience & Qualifications:

• 3+ years industry experience and an advanced degree (PhD preferred) in a quantitative field, such as Statistics, Operations Research, Economics, Computer Science, or equivalent quantitative fields.

• Expert in one or multiple areas of machine learning, causal inference, and operations research.

• Comfortable to analyze complicated data sets to generate insights to guide effective solution development.

• Passion for solving exciting and meaningful real-world problems using principled techniques and practices.

• Focus on problem solving with pragmatical methods.

• Understanding of modern machine learning and data science techniques and their mathematical underpinning.

• Ability to communicate clearly and effectively to cross functional partners of varying technical levels.

• Collaborates well in a team and sensitive to clients' needs, while developing warm relationships and building trust with stakeholders

• Proficiency in one of the tools such as Python, Scala, etc

• Proficiency with big data ecosystem (Hadoop/Spark)

• Experience in pricing is a plus.

• Experience with large scale marketplace experiment design and analysis is a plus.

#LI-MC1

The total cash range for this position in Austin is $137,500.00 to $192,500.00. Employees in this role have the potential to increase their pay up to $220,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.

Starting pay for this role will vary based on multiple factors, including location, available budget, and an individual's knowledge, skills, and experience. Pay ranges may be modified in the future.

Expedia Group is proud to offer a wide range of benefits to support employees and their families, including medical/dental/vision, paid time off, and an Employee Assistance Program. To fuel each employee's passion for travel, we offer a wellness & travel reimbursement, travel discounts, and an International Airlines Travel Agent (IATAN) membership. View our full list of benefits.

Accommodation requests

If you need assistance with any part of the application or recruiting process due to a disability, or other physical or mental health conditions, please reach out to our Recruiting Accommodations Team through the Accommodation Request.