Audible Content Data Science team partners with business, technology, and product leaders to solve problems related to Content Understanding, Content Evaluation, and Recommendations, relying on Natural Language Processing (NLP), ML, Deep Learning, LLMs/GenAI. We operate in an agile environment in which we own the life cycle of research, design, model development and, often, deployment.

Mining, data modeling, natural language processing, and machine learning to extract and manipulate data from multiple large data sources and deliver predictive models that inform solutions for in-house teams (i.e. pharmacy pricing, medical costs, risk scores, onboarding) and customer engagement across critical journeys (i.e. calories, heart rate, breast cancer, maternity.

Experience in working with unstructured data, such as text or image data, and applying natural language processing or computer vision techniques. In-depth expertise in statistical analysis, machine learning algorithms, and predictive modeling techniques.

Time series Analysis Different algorithms like Decision Tree and Random Forest algorithms, Support Vector Machine Algorithm Deep Learning and Computer vision Neural Networks, Tensor Flow and Keras Natural language processing (NLP) and Text mining Market Basket Analysis NLP with Python, Sentiment analysis Linear regression, Scikit Learn, Confusion matrix, Decision tree, Ensemble approach Random forest, Cross validation.

Strong experience with computer vision (CV) or natural language processing (NLP) using deep learning. Proficient in Digital & Computational Pathology, AI, Machine Learning, Data Science, Image Processing and Analysis.

Demonstrated coursework / real world application of analytical methods such as Regression, Naïve-Bayes, Decision trees, experimental designs, support vector machines, machine learning and text mining, Natural Language Processing.

Predictive Modeling, Machine Learning, Deep Learning, 3rd Party & 1st Party data integration, Natural Language Processing (NLP), Computer Vision and Digital Image Processing.

AI/ML Development : Oversee the design, development, and deployment of scalable AI and ML models and platforms to support various biotech applications, including predictive modeling, image analysis, natural language processing, and computational biology.

The internship will involve using data science techniques that include predictive modeling, machine learning, text mining, segmentation and clustering to help in developing solutions to increase revenue and improve Member Experience.

4+ years of relevant data analytics experience in one or more of the following areas: statistical modeling, machine learning, natural language processing (NLP), and geographical information system (GIS.

Req Demonstrated expertise in applying advanced statistical and machine learning techniques to real-world problems, such as predictive modeling, natural language processing, computer vision, or recommender systems at scale.

Demonstrated expertise in applying advanced statistical and machine learning techniques to real-world problems, such as predictive modeling, natural language processing, computer vision, or recommender systems at scale.

Artificial intelligence, including computer vision, natural language processing, human language technologies, multimodal sense-making, and other AI/ML based systems. Computational social science (including agent-based modeling and simulation, social media analysis, behavioral economics, game theory, predictive modeling.

Expertise in various data science methods and techniques, such as supervised and unsupervised learning, natural language processing, computer vision, deep learning, optimization, and simulation.

Publications or research experience in the field of data science, machine learning, or related areas. Master's or Ph. D. degree in a quantitative field such as Data Science, Computer Science, Statistics, Mathematics, or a related fiel.