Data Scientist

San Francisco OR Remote US/Canada

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Scribd

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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 TeamApplied Research works on a variety of ML and NLP projects like spam detection, language identification, and text embeddings. We are a full-stack data science team that runs exploratory analyses, sizes business impact, creates data pipelines, presents projects, and builds models from prototype to production. We work on Scribd’s unique and massive dataset consisting of hundreds of millions of documents, books, audiobooks, articles, and podcasts.
About YouYou are a curious person who enjoys tackling hard problems. You got into data science because you love being at the forefront of what is possible and the idea of building intelligent systems that scale to reach millions of people inspires you. As a data scientist, you have a deep appreciation for project-based learning and picking up new skills of all kinds, both technical and interpersonal. 
You will- Build reproducible and scalable ML models in Python and Spark.- Work with large, complex data sets to solve difficult, non-routine analysis problems. - Develop comprehensive knowledge of Scribd’s data structures and metrics. - Take complicated concepts, systems, and processes and clearly communicate what matters to stakeholders.
You Have- 2+ years of experience in data science or ml engineering.- BA/BS in a quantitative discipline. Master’s or Doctorate degree preferred.- Intermediate level or greater experience with Python and SQL.- Sufficient statistical skills to make effective ML modeling tradeoffs. - Experience with at least one of the following fields: natural language processing, deep learning, computer vision, and bayesian statistics.- A keen interest in learning what’s necessary to solve a business problem and make a positive business impact.- A sense of energy, passion and personal responsibility, taking on whatever you can for the good of the team.- Preferred: Experience building data pipelines and models with Spark.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.
Job region(s): Remote/Anywhere North America
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