Lead Data Scientist, AdTech

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop models to determine which auto insurance carriers are displayed to shoppers on the click wall.

• Enhance the matching process between users and carriers to improve conversion rates and optimize carrier bidding strategies.

• Manage the purchasing models related to media expenditure and ensure they align with Return on Ad Spend (ROAS) objectives.

• Integrate carrier conversion metrics, which include quotes, policies, and acquisition costs, into production models.

• Define business challenges and identify, create, and validate corresponding models.

• Oversee the deployment and monitoring of models.

• Independently manage media buying for the insurance marketplace by day 60.

• Provide click-wall matching, production conversion models, scalable automated bidding solutions, strategies for repeat users and sparse sessions, as well as models that can adapt to drift and evolving objectives.

• Collaborate with a Machine Learning engineer to implement production systems.

• Gradually broaden the focus from auto insurance to encompass additional verticals.

⛳️ Requirements

• A minimum of 5 years of practical data science experience with a demonstrable impact on business outcomes.

• Experience where models influenced decisions regarding paid media, including spending, bidding, or ad serving; pure attribution and reporting do not qualify.

• Proficient in articulating work related to loss functions and architectural decisions, including core concepts of neural networks.

• Skilled in managing late labels, sparse features, or drift.

• Capable of owning models throughout their lifecycle, from data discovery to deployment and ongoing monitoring.

• Expertise in Python and SQL.

• Experience with real advertising budgets in live auctions is a plus.

• Familiarity with large-scale ranking or recommendation systems is desirable.

• Ability to communicate trade-offs to non-technical stakeholders is advantageous.

• Experience in designing for auction feedback loops, selection bias, and exploration is preferred.

• Practical experience with neural network implementation is a plus.

• Knowledge of AWS tools for data science, including ECS, S3, Athena, and SageMaker is preferred.

• Experience with CI/CD deployment is a plus.

🏝️ Benefits

• Profit-sharing bonus.

• Competitive benefits package.

• Remote-first work environment.

• Diverse and inclusive team culture.

• Equal Employment Opportunity company.