Medallia is the pioneer and market leader in Experience Management. Our award-winning SaaS platform, Medallia Experience Cloud, leads the market in the understanding and management of experience for candidates, customers, employees, patients, citizens and residents.

We are more than a software company. We want to be known as a company that does the right thing, no matter the challenge or controversy. We are committed to creating a culture that values every person and every experience. Individual life experiences shape the way we interact with the world, which is why we encourage people to bring their whole selves to work each day. The strength of our global workforce is the most significant contributor to our success.

We believe: Every Experience Matters. Talent is Everywhere. All Belong Here.

At Medallia, we hire the whole person.

The Team
Data Science at Medallia brings together researchers, engineers and designers, to build adaptive, collaborative technologies that are focused on enabling action at all levels of an organization. We drive both platform and product development, so that our customers can interact with their customers in satisfying and emotionally connected ways, regardless of scale.

• As a Senior Research Data Scientist on the Data Science team, you will lead cutting edge research in artificial intelligence and machine learning applications in the areas of natural language understanding/generation, action recommendation and representation learning.
• Run data science projects independently.
• Partner with engineering, design and product teams to deliver solutions to orchestrating, understanding and improving customer experiences in real time.
• Collaborate with a team of brilliant engineers and scientists responsible for bringing intelligent features to all corners of Medallia’s product offerings.

Minimum Qualifications

• Bachelor’s degree in Computer Science or related technical discipline or equivalent practical experience
• 3 years of relevant work experience, including software development experience or 1 year of relevant work experience with a PhD in Computer Science or related technical discipline
• 3 years of work or educational experience in Machine Learning or Artificial Intelligence
• Demonstrated proficieny Python, and detailed working knowledge of the packages and frameworks associated with data wrangling, analysis, and machine learning, e.g. Tensorflow, Pytorch, Sklearn, Spacy, Pandas, PySpark

Preferred Qualifications

• MS or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical discipline
• Strong communication skills, especially in describing technical methods and results to both technical and non-technical audiences
• Experience with utilizing, training,or fine-tuning large language models
• Extensive experience in one or more of the following areas: Natural Language Processing for text understanding, classification, summarization, pattern recognition, and/or recommendation/ranking systems; Representation learning for unstructured data types (images, text), multiple data sources (e.g. EMR/EHR), or event sequences (e.g. customer journeys)
• Experience utilizing machine learning platforms e.g. Kubeflow, SageMaker, VertexAI, Databricks

At Medallia, we celebrate diversity and recognize the value it brings to our customers and employees. Medallia is proud to be an equal opportunity workplace and is an affirmative action employer. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, genetic information, disability, veteran status, or any other applicable status protected by state or local law. Individuals with a disability who need an accommodation to apply please contact us at ApplicantAccessibility@medallia.com. For information regarding how Medallia collects and uses personal information, please review our Privacy Policies. Applications will be accepted for 30 days from the date this role was posted or until the role has been filled.