We are a global technology company that combines deep industry expertise, user-centric design, and world-class software engineering. With its human-centric approach, the company creates digital products that empower users, deliver business value, and make an impact on society.
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:
● 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.
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