Scribd
San Francisco
201-500 employees
The best audiobooks, books, and more, available anywhere. Join us on our mission to change the way the world reads.
Data Scientist - Personalization
Data Scientist - Personalization
This job is no longer open
At Scribd (pronounced “scribbed”), we believe reading is more important than ever. Join our cast of characters as we work to change the way the world reads by building the world’s largest and most fascinating digital library: giving subscribers access to a growing collection of ebooks, audiobooks, magazines, documents, Scribd Originals, and more. In addition to works from major publishers and top authors, our community includes over 1.5M subscribers in nearly every country worldwide.
About the team
The Applied Research - Personalization team’s goal is to comprehensively understand our users, create representations of their behavior and complex interactions with our content, and deliver user models that drive ML product features. This is accomplished as a team of aligned, talented and collaborative data scientists.
About you
You are excited by the opportunities and challenges of working on Scribd’s unique and massive dataset consisting of interactions of hundreds of millions of pieces of content with hundreds of millions of users. You enjoy representing our users in models and measure your contribution in terms of user impact.
You will
- Deeply understand our data and define how user interactions create signals for ML models.
- Take full advantage of our scale of data. For example, use Transformers and other state-of-the-art deep learning architectures to extract rich user embeddings.
- Optimize personalization for new users by integrating other sources of data and innovating on models. For example, use Bayesian methods to smoothly combine priors about user segments with data-rich modeling approaches.
- Increase the effectiveness of the team on a technical and problem-solving level through thoughtful collaboration with other Data Scientists, Machine Learning Engineers, and System Engineers.
You Have
- 2+ years of experience in data science or related field.
- Bachelor's degree in a quantitative discipline. Master’s or Doctorate degree preferred.
- Interested or experience with personalization features, user modeling
- Intermediate level or greater experience with Python.
- Experience with SQL and Spark is a plus.
- Exposure to TensorFlow and embedding-based deep learning architectures is a plus.
Benefits, Perks, and Wellbeing at Scribd
• Healthcare Benefits: Scribd pays 100% of employee’s Medical, Vision, and Dental premiums and 70% of dependents
• Leaves: Paid parental leave, 100% company paid short-term/long-term disability plans and milestone Sabbaticals
• 401k plan through Fidelity, plus company matching with no vesting period
• Equity - Every employee is an owner in Scribd!
• Generous Paid Time Off, Paid Holidays, Flexible Sick Time, Volunteer Day + office closure between Christmas Eve and New Years Day
• Referral bonuses
• Professional development: generous annual budget for our employees to attend conferences, classes, and other events
• Company-wide Diversity, Equity, & Inclusion programs which include learning & development opportunities, employee resource groups, and hiring best practices.
• Learning & Development and Coaching programs
• Monthly Wellness, Connectivity & Comfort Benefit
• Concern mental health digital platform
• Work-life balance flexibility
• Company events + Scribdchats
• Free subscription to Scribd + gift memberships for friends & family
Want to learn more? Check out our office and meet some of the team at www.linkedin.com/company/scribd/life
Scribd is committed to equal employment opportunity regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.
We encourage people of all backgrounds to apply. We believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful.
This job is no longer open