This is a Fully Remote Job About Our Client: The organization operates in the digital media and consumer acquisition industry, focusing on connecting discovery, intent, and performance across consumer brands in finance, travel, and entertainment. It addresses the challenge of customer acquisition by leveraging a portfolio of brands to reach tens of millions monthly, converting that attention into measurable outcomes for partner companies. The company maintains a remote-first team distributed across multiple countries with headquarters in South Florida. About the Opportunity: The LEAD DATA SCIENTIST, ADTECH, role is responsible for building and managing predictive models that optimize which auto insurance carriers are presented to users, aiming to maximize conversion rates and return on ad spend. This position directly influences media buying decisions and model performance in a dynamic environment characterized by delayed conversion data, sparse sessions, and market fluctuations. The role requires end-to-end model ownership from problem framing through deployment and ongoing monitoring, contributing crucially to the effectiveness and scalability of the organization's advertising marketplace. Responsibilities: Develop models to determine carrier selection for auto insurance shoppers to optimize conversion. Manage media buying models aimed at achieving targeted return on ad spend. Integrate and utilize delayed and complex conversion data within production environments. Address challenges related to sparse user sessions and repeat users. Monitor and maintain model performance despite market drift and changing objectives. Collaborate with ML engineers for production deployment. Expand modeling scope to additional verticals over time. Requirements: Minimum of 5 years of applied data science experience with clear business impact. Proven experience with models that drive paid media decisions such as spend, bidding, or ad serving. Deep understanding of model architecture, including neural network fundamentals and loss functions. Experience handling training data influenced by bidding strategies, late labels, sparse features, and data drift. Capability to own models from data acquisition through deployment and monitoring. Proficiency in Python and SQL. Pay Range and Compensation Package: Base salary from $175,000 to $200,000 per year, paid semi-monthly. Compensation includes base salary, profit-sharing bonus, and benefits. Equal Opportunity Statement: Our client is an equal opportunity employer. They celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, or national origin. Note: TalentHop is a recruitment partner of this role. Please note that all employment decisions, including candidate assessment, interviews, hiring, compensation, and employment terms, are made exclusively by the hiring employer.