Principal Tech Lead

Overview: Cvent is a leading meetings, events, and hospitality technology provider with more than 4800 employees and ~22,000 customers worldwide, including 53% of the Fortune 500. Founded in 1999, Cvent delivers a comprehensive event marketing and management platform for marketers and event professionals and offers software solutions to hotels, special event venues and destinations to help them grow their group/MICE and corporate travel business. Our technology brings millions of people together at events around the world. In short, we’re transforming the meetings and events industry through innovative technology that powers the human connection. The DNA of Cvent is our people, and our culture has an emphasis on fostering intrapreneurship – a system that encourages Cventers to think and act like individual entrepreneurs and empowers them to act, embrace risk, and make decisions as if they had founded the company themselves. At Cvent, we value the diverse perspectives that everyone brings. Whether working with a team of colleagues or with clients, we ensure that we foster a culture that celebrates differences and builds on shared connections. About the Role: Cvent Tech Lead team is looking to hire Principal Tech Lead / Data Scientist. The selected incumbent will typically focus on data analysis, ML modeling and Generative AI data products. He/she would be involved in analysis for various areas across the organization i.e., in Sales, Marketing, Technology, & Client Services. The person is expected to understand end-to-end business processes. Independently extract, prepare, and analyze data to support business initiatives (e.g., revenue, profitability, performance, variance analysis etc.). Develop solutions with minimal support. Develop strategies using techniques and algorithms of data analysis & machine learning models for making meaningful and actionable recommendations. Present and share data with other team members and to leadership. In This Role, You Will: Stakeholder Management Collaborate with stakeholders at different levels to understand current processes, data being generated and identify optimization or growth opportunities. Strong ability to augment data and insights to drive scale and efficiency for our stakeholders and partners. Data Analysis & Modeling Person would be required to work individually or as part of a team on data science & analytical projects. Define business problem and translate it into statistical/analytical problem. He/she would be developing statistical/machine learning models using various techniques (supervised, unsupervised, semi-supervised) and technologies including but not limited to Python, SQL, Databricks, Snowflake, ChatGPT, AWS, Excel, Sigma etc. Person would be performing machine learning research on various types of data (numeric, text, audio, video, image). Person would be required to leverage LLM’s for various tasks like text summarization and categorization, data gathering and analysis for generating interactive insights. Person would be required to thoroughly document your work as well as results, to the extent that another data scientist can replicate them. Person would be required to Create data products on Streamlit and Sigma backed by machine learning models in conjunction with various LLM Models. Here's What You Need: A bachelor’s degree in a quantitative field (natural sciences, math, statistics, computer science). 4 - 7 years of experience working as a data scientist in industry. Conceptual and technical understanding of machine learning, including model training and evaluation Strong analytical and problem-solving skills with an ability to distill data into important, relevant insights for business leaders. Deep Conceptual and technical understanding of machine learning: Regression modelling (linear/logistic), supervised and unsupervised classification (Clustering), tree-based techniques (Decision trees /Random Forest) etc. Understanding of prompt engineering best practices and the ability to design effective prompts for various LLM applications, including few-shot learning and chain-of-thought reasoning. Deep Understanding of emerging LLM technologies, such as multimodal LLMs, Agentic frameworks, and techniques for model quantization and distillation for efficient deployment. In-depth familiarity with the Linux operating system and command-line work. Proficiency in Python, SQL (Snowflake), AWS Cloud, Databricks, Streamlit, MS-Excel, and MS-PowerPoint. Strong and articulate verbal and written communication skills with attention to precision of language and ability to organize information logically. Stakeholder Management Collaborate with stakeholders at different levels to understand current processes, data being generated and identify optimization or growth opportunities. Strong ability to augment data and insights to drive scale and efficiency for our stakeholders and partners. Data Analysis & Modeling Person would be required to work individually or as part of a team on data science & analytical projects. Define business problem and translate it into statistical/analytical problem. He/she would be developing statistical/machine learning models using various techniques (supervised, unsupervised, semi-supervised) and technologies including but not limited to Python, SQL, Databricks, Snowflake, ChatGPT, AWS, Excel, Sigma etc. Person would be performing machine learning research on various types of data (numeric, text, audio, video, image). Person would be required to leverage LLM’s for various tasks like text summarization and categorization, data gathering and analysis for generating interactive insights. Person would be required to thoroughly document your work as well as results, to the extent that another data scientist can replicate them. Person would be required to Create data products on Streamlit and Sigma backed by machine learning models in conjunction with various LLM Models. A bachelor’s degree in a quantitative field (natural sciences, math, statistics, computer science). 4 - 7 years of experience working as a data scientist in industry. Conceptual and technical understanding of machine learning, including model training and evaluation Strong analytical and problem-solving skills with an ability to distill data into important, relevant insights for business leaders. Deep Conceptual and technical understanding of machine learning: Regression modelling (linear/logistic), supervised and unsupervised classification (Clustering), tree-based techniques (Decision trees /Random Forest) etc. Understanding of prompt engineering best practices and the ability to design effective prompts for various LLM applications, including few-shot learning and chain-of-thought reasoning. Deep Understanding of emerging LLM technologies, such as multimodal LLMs, Agentic frameworks, and techniques for model quantization and distillation for efficient deployment. In-depth familiarity with the Linux operating system and command-line work. Proficiency in Python, SQL (Snowflake), AWS Cloud, Databricks, Streamlit, MS-Excel, and MS-PowerPoint. Strong and articulate verbal and written communication skills with attention to precision of language and ability to organize information logically.