Data Scientist II

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

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)