Data Scientist II

About The Company 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 work. In 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. 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. Benefits, Perks, And Wellbeing At Scribd 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. 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. #J-18808-Ljbffr