$ cat jobs/ai-ml-data-scientist-mts-2-3-4-thenielsencompany-df57d8dd6018.json
AI / ML Data Scientist -MTS 2 / 3/ 4
At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future. At Nielsen, we are seeking a Data Scientist to join our team. Are you passionate about pushing the boundaries with the latest advancements in AI/ML? Does the prospect of applying cutting-edge AI research to develop industry-defining software solutions for audience measurement excite you? In this role, you will be at the forefront of our mission, leveraging sophisticated machine learning and AI to deliver a comprehensive understanding of audience behavior. You will architect and implement AI/ML systems that unlock novel insights from complex audience data. Skills : ● Strong understanding and experience in classical Machine Learning algorithms and techniques. ● Demonstrated ability to work with high motivation and agility in a dynamic environment. ● Education: Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. ● Programming Languages: ○ Python (Expert): Strong proficiency in Python with extensive experience in libraries such as: Scikit-learn, Pandas & NumPy, Matplotlib, Seaborn, SciPy. ○ Deep Learning Libraries: Strong proficiency in Tensor flow, Keras and Pytorch. . ● Classical Machine Learning Expertise: ○ Thorough understanding of supervised learning (e.g., Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Machines lik
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