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Google Ads Customer: The current manual decision model for assigning customers to sales channels is limited by historical data and a narrow understanding of what drives customer performance. This role aims to automate and enhance these assignments by developing a causal ML model and uncovering new data patterns to revolutionize client management.

Requirements

• coding (e.g., Python, R, SQL)
• querying databases
• statistical analysis
• Experience designing and implementing causal ML models.
• Experience designing and building large datasets for ML modeling.
• Experience with statistical modeling packages.

Responsibilities

• Develop core dataset and signal repository, using SQL or other scripting language, for Machine Learning.
• Develop expansive dataset that describes the Google Ads Customer, in as much detail as reasonable, that will enable us to establish causal effects from our business efforts.
• Investigate, ideate, build, and streamline major signal repository to enable ML and unique insights.
• Analyze data, partner with other Business Data Scientists, to help us understand drivers of historical business performance.
• Leverage insights to determine areas of opportunity for improvement in business treatment.
• Build causal ML models to improve business channel assignments and treatment.
• ideating, discovering, and integrating hundreds, if not thousands, of data signals, ensuring they are perfectly structured for our causal ML problem.

Other

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
• 4 years of experience using analytics to solve product or business problems
• Excellent presentation skills.
• strategic thinker, using your business and product judgment to guide our data strategy.
• problem-solving skills