Senior Applied Scientist

Key Skills

Java Programming, Python Programming, Machine learning techniques, Natural Language Processing (NLP), NoSQL, TensorFlow, Scikit-learn, spaCy, Elasticsearch, PostgreSQL

Job Description

The Qualtrics XM Platform™ is a system of action that helps businesses to attract customers who stay longer and buy more, to engage and empower employees to do the best work of their lives, to develop breakthrough products people love, and to build a brand people can’t imagine living without.

Joining Qualtrics means becoming part of a team bold enough to chase breakthrough experiences - like building a technology that will be a force for good. A team committed to diversity, equity, and inclusion because of a conviction that every voice holds value, with a vision for representation that matches the world around us and inclusion that far exceeds it. You could belong to a team whose values center on transparency, being all in, having customer obsession, acting as one team, and operating with scrappiness. All so you can do the best work of your career.

We believe every interaction is an opportunity. Are we yours?

About the role:

As an Applied Scientist at Qualtrics, you’ll collaborate with software engineers and other data scientists to develop customer feedback analytics using NLP and Machine Learning. Our large and diverse data sources include social media, surveys, online reviews, and call center transcripts from major companies across the globe. You will apply methods such as text classification, topic modeling, clustering, and transfer learning using both traditional and deep learning approaches. This is an applied research role with a practical focus on adapting methods from open source and the research literature for high-volume production pipelines.

About you:

You have a proven record of developing ML components for robust and scalable data-intensive applications. Specifically:

• 5+ years work experience.
• Advanced training in Natural Language Processing and Machine Learning.
• Proficiency in Python, including the ML ecosystem and software development tools.
• Knowledge and experience with traditional ML techniques such as Logistic Regression and Random Forests.
• Knowledge and experience with architectures such as LSTMs and Transformers in at least one deep learning framework (TensorFlow, PyTorch).
• Preferably, experience working with data in other languages besides English.
• Familiarity with software development practices such as containerization (docker), dependency management, testing, and API frameworks.

Ability to communicate results both to peers and to company leadership.