Job
Description
At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in artificial intelligence and machine learning at PwC will focus on developing and implementing advanced AI and ML solutions to drive innovation and enhance business processes. Your work will involve designing and optimising algorithms, models, and systems to enable intelligent decision-making and automation. Years of Experience: Candidates with 15+ years of hands on experience Must Have Familiarity with the CCaaS domain, contact center operations, customer experience metrics, and industry-specific challenges Understanding of conversational (chats, emails and calls) data to train Conversational AI systems In-depth knowledge of CCaaS platforms like NiceCX, Genesys, Cisco etc., including their architecture, functionalities, and integration capabilities Familiarity with contact center metrics, such as average handle time (AHT), first call resolution (FCR), and customer satisfaction (CSAT) Familiarity with sentiment analysis, topic modeling, and text classification techniques Proficiency in data visualization tools like Tableau, Power BI, Quicksight and others Understanding of cloud platforms (e.g., AWS, Azure, Google Cloud) and their services for scalable data storage, processing, and analytics NLU Verticals Expertise: ASR generation, SSML modeling, Intent Analytics, conversational AI testing, Agent Assist, Proactive Outreach Orchestration, and Generative AI Apply advanced statistical and machine learning techniques to analyze large datasets and develop predictive models and algorithms, enhancing contact center performance. Nice To Have Proficiency in programming languages such as Python/Pyspark/R/SQL Strong understanding of data science principles, statistical analysis, and machine learning techniques. Experience in predictive modeling Skilled in techniques like regression analysis, time series forecasting, clustering and NLP techniques Knowledge of distributed computing frameworks like Hadoop and Spark for processing large volumes of data. Understanding of NoSQL databases (e.g., MongoDB, Cassandra) for handling unstructured and semi-structured data. Awareness of data security best practices, encryption techniques, and compliance regulations (e.g., GDPR, CCPA). Understanding of ethical considerations in data science and responsible AI practices. Educational Background BE / B.Tech / MCA / M.Sc / M.E / M.Tech / MBA Show more Show less
