Data Scientist II

Scribd, Inc. is on a mission to advance human understanding. Our four products — Scribd®, Slideshare®, Everand™, and Fable — help billions of people across the globe move beyond access and into insight, application, and expertise.

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.

We believe the best work happens when individual flexibility is balanced with meaningful community connection. Scribd Flex empowers employees to choose the workstyle and location that support their best performance, while committing to intentional in-person moments that strengthen collaboration and culture. Occasional in-person attendance is required for all Scribd, Inc. employees, regardless of location.

So what are we looking for in new team members? At Scribd, Inc., we hire for “GRIT.” Traditionally defined as the intersection of passion and perseverance toward long-term goals, GRIT reflects the mindset we expect from every employee. For us, it also serves as a practical framework for how we work: setting and achieving Goals, delivering Results within your role, contributing Innovative ideas and solutions, and strengthening the broader Team through collaboration and attitude.

This posting reflects an approved, open position within the organization.

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 it’s 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.

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.

• 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 Num Py 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

• 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…