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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.
When it comes to workplace structure we believe in balancing individual flexibility and community connections. Its through our flexible work benefit Scribd Flex that employees in partnership with their manager can choose the daily work-style that best suits their individual needs. A key tenet of Scribd Flex is our prioritization of intentional in-person moments to build collaboration culture and connection. For this reason occasional in-person attendance is required for all Scribd employees regardless of their location.
So what are we looking for in new team members Well we hire for GRIT. The textbook definition of GRIT is demonstrating the intersection of passion and perseverance towards long term goals. At Scribd we are inspired by the potential that this can unlock and ask each of our employees to pursue a GRIT-ty approach to their a tactical sense GRIT is also a handy acronym that outlines the standards we hold ourselves and each other to. Heres what that means for you: were looking for someone who showcases the ability to set and achieve Goals achieve Results within their job responsibilities contribute Innovative ideas and solutions and positively influence the broader Team through collaboration and attitude.
About the team
The Applied Research team is a group of data scientists and content specialists who are experts in leveraging machine learning natural language processing and generative AI models to develop solutions which deliver value to our users and business.
We act as a key driver for innovation whether its in product surface experimentation metadata generation or model development. Along with Product and Engineering partners we design solutions and collaborate in cross-functional squads to maximize business impact.
Our areas of impact include content enrichment representation learning recommendations search translation and many others applied to diverse media across text image and audio. We operate at a scale of hundreds of millions of documents millions of users and billions of user interactions.
Role Overview
We are seeking a Data Scientist II with experience developing and deploying machine learning models. You will help design and implement high impact AI and ML systems. We work in cross-functional teams collaborating with Machine Learning Engineers Data Engineers and Product. We are seeking a curious and collaborative individual with an eye for simplicity end-end visibility and impact and that is excited about building models using massive amounts of data using language models and deploying models.
Responsibilities
Focus on a variety of content classification use cases leveraging everything from traditional NLP to sophisticated LLMs and generative models
Investigate methods of solving our most challenging problems at Scribd at scale
Collaborate with other Data Scientists Machine Learning Engineers and ML Data Engineers on cross-functional 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
Align with stakeholders through written and verbal communications methods on the approaches and results of projects while writing detailed accurate and concise project documentation
Requirements
3 years of post qualification experience developing machine learning models working with systems at scale and deploying to production environments.
Proficiency in Python.
Hands-on experience building ML pipelines and working with distributed data processing frameworks like Apache Spark Databricks or similar.
Intermediate level in at least three of these fields: classification algorithms natural language processing search information retrieval named entity recognition deep learning generative models.
Intermediate level or greater experience with SQL or PySpark.
Bachelors or Masters in relevant quantitative discipline including but not limited to Statistics Computer Science Data Science Artificial Intelligence or another field with a strong quantitative focus.
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 the state of California the reasonably expected salary range is between $118000 minimum salary in our lowest geographic market within California to $184000 maximum salary in our highest geographic market within California.
In the United States outside of California the reasonably expected salary range is between $97000 minimum salary in our lowest US geographic market outside of California to $175000 maximum salary in our highest US geographic market outside of California.
In Canada the reasonably expected salary range is between $123000 CADminimum salary in our lowest geographic market to $164000 CADmaximum 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 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.
Are you currently based in a location where Scribd is able to employ you
Employees must have their primary residence in or near one of the following cities. This includes surrounding metro areas or locations within a typical commuting distance:
United States:
Atlanta Austin Boston Dallas Denver Chicago Houston Jacksonville Los Angeles Miami New York City Phoenix Portland Sacramento Salt Lake City San Diego San Francisco Seattle Washington D.C.
Canada:
Ottawa Toronto Vancouver
Mexico:
Mexico City
Benefits Perks and Wellbeing at Scribd
*Benefits/perks listed may vary depending on the nature of your employment with Scribd and the geographical location where you work.
Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees
12 weeks paid parental leave
Short-term/long-term disability plans
401k/RSP matching
Onboarding stipend for home office peripherals accessories
Learning & Development allowance
Learning & Development programs
Quarterly stipend for Wellness WiFi etc.
Mental Health support & resources
Free subscription to the Scribd Inc. suite of products
Referral Bonuses
Book Benefit
Sabbaticals
Company-wide events
Team engagement budgets
Vacation & Personal Days
Paid Holidays ( winter break)
Flexible Sick Time
Volunteer Day
Company-wide Employee Resource Groups and programs that foster an inclusive and diverse workplace.
Access to AI Tools: We provide free access to best-in-class AI tools empowering you to boost productivity streamline workflows and accelerate bold innovation.
Want to learn more about life at Scribd want our interview process to be accessible to everyone. You can inform us of any reasonable adjustments we can make to better accommodate your needs by emailing about the need for adjustments at any point in the interview process.
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.
Full-Time