Data Scientist II

at  Scribd

At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. Join our team as we create a world of stories and knowledge, democratize the exchange of ideas and information, and empower collective expertise through our three products: Everand, Scribd, and Slideshare.
We support a culture where our employees can be real and be bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer.
Our flexible work benefit - Scribd Flex - enables employees, in partnership with their manager, to choose the daily work-style that best suits their individual needs. As an organization, we prioritize collaboration and intentional in-person moments to build culture and connection. For this reason, occasional in-person attendance is required for all Scribd employees, regardless of their location.

REQUIREMENTS

• 2-3 years of experience of developing and deploying machine learning models
• Intermediate level or greater experience with SQL or Spark
• Intermediate to advanced knowledge with Python
• Intermediate level in at least three of these fields: classification algorithms, natural language processing, search, information retrieval, named entity recognition, deep learning, computer vision, bayesian statistics or frequentist statistics
• A keen interest in learning what’s necessary to solve a business problem and make a positive business impact.
• Bachelors or Masters in relevant quantitative discipline (e.g. Computer Science, Software Engineering, Data Science, Machine Learning, Artificial Intelligence, Computational Linguistics, Mathematics, Statistics, Economics)
  At Scribd, your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. San Francisco is our highest geographic market in the United States. In the state of California, the reasonably expected salary range is between $121,000 [minimum salary in our lowest geographic market within California] to $196,250 [maximum salary in our highest geographic market within California].
  In the United States, outside of California, the reasonably expected salary range is between $100,000 [minimum salary in our lowest US geographic market outside of California] to $186,500 [maximum salary in our highest US geographic market outside of California].
  In Canada, the reasonably expected salary range is between $125,500 CAD[minimum salary in our lowest geographic market] to $186,000 CAD[maximum salary in our highest geographic market].
  We carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package.

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, AND BELIEVE THAT A DIVERSITY OF PERSPECTIVES AND EXPERIENCES CREATE A FOUNDATION FOR THE BEST IDEAS. COME JOIN US IN BUILDING SOMETHING MEANINGFUL.

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Remote employees must have their primary residence in: Arizona, California, Colorado, Connecticut, Delaware, DC, Florida, Georgia, Hawaii, Iowa, Massachusetts, Maryland, Michigan, Missouri, Nevada, New Jersey, New York, Ohio, Oregon, Tennessee, Texas, Utah, Vermont, Washington, Ontario (Canada), British Columbia (Canada), or Mexico.
#LI-Remot

• In your first year, you’ll be focusing on a variety of content classification use cases, leveraging everything from traditional NLP to sophisticated fine tuning of LLMs
• Investigate methods of solving our most challenging problems at Scribd, at scale
• Work with other Data Scientists and Machine Learning Engineers to operationalize data science projects
• Leverage any algorithm at your disposal: from classical Scikit-learn and NumPy models to custom Neural Networks in PyTorch to third party LLM APIs
• Process massive amounts of data with Python, SQL and Spark
• Educate stakeholders through written and verbal communications methods on the approaches and results of projects, while writing detailed, accurate and concise project write ups