Are you a Senior Data and Applied Scientist who is passionate about infusing AI into Microsoft Dynamics 365 apps? Does the challenge of applying state-of-the-art ML algorithms to solve real-world business problems sound exciting? Would you like to acquire hands-on experience of working with product teams to ship products and make business impact? If you answered "yes" to these questions, we want to talk to you about joining the Data Analytics (DA) team, under Customer Care Application (CCA) and Business Application Group (BAG). Our mission is to empower our customers to make data driven decisions through integrated analytics and adaptive experiences. We are responsible for designing and developing ML based solutions to support product teams in CCA Customer Service vertical. You will work with a diverse group of data scientists, software engineers and product/project managers, and have access to broad data science expertise and tools to build delightful products for Dynamics 365 customers
Lead collaboration across multiple disciplinary teams to build an AI/ML based solution for voice analytics in Omni-Channel Sentiment Analysis (OCSA), an integral part of Dynamics 365's Customer Service product family. Develop trend and anomaly detection features in Omni-Channel Sentiment Analysis (OCSA) to provide sentiment driven insihgts, in real-time or offline mode. Be an AI/ML domain expert in Customer Service Data Science team to help accelerating development of product features through collaboration with focus on customer impacts. Lead exploration of new AI/ML algorithms, e.g. deep learning,
Bachelor/MS/PhD degree in math, statistics, physics, engineering, or computer science 3 years of experience in Machine Learning, Artificial Intelligence or Statistics (Python or R), e.g. classification, regression, optimization, deep learning or experimentation 2 years of experience in Natural Language Processing (NLP) and Text Analytics, e.g. topic modeling and named entity extraction Preferred qualifications: Proficiencies in Python, R or other ML related programming languages Familiarity with Speech Recognition related technologies, including speech to text transcription, acoustic feature generation,