Product Activation Incentives Technical Analyst, YouTube

Product Activation Incentives Technical Analyst, YouTube corporate_fare YouTube place San Bruno, CA, USA ; New York, NY, USA bar_chart Mid Mid Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area. info_outline

X Note:

By applying to this position you will have an opportunity to your preferred working location from the following: San Bruno, CA, USA; New York, NY, USA .

Minimum qualifications:

Bachelor's degree in Engineering, Computer Science, a related technical field, or equivalent practical experience. 3 years of experience using Python or similar languages for building, validating, and deploying predictive models (e.g., propensity, classification). 3 years of experience with data analysis, relational databases, and SQL.

Preferred qualifications:

Master's degree in Data Science, Engineering, Mathematics, Statistics, Economics, Applied Science, or a related field. 5 years of experience in the technical analytics space (data science, data engineering, product analyst, business analyst, business intelligence, etc.). Experience using Python for the full modeling lifecycle, from feature engineering and model selection to productionalization. Experience drawing insights from data sets using SQL to drive both business and technology decisions. About the job As a Product Activation Incentives Technical Analyst within this team, you will be part of the engine driving the activation of key YouTube products globally and at scale. In this role, you will be responsible for improving our recommendations and pricing models to maximize the impact of partner managers. You will be an individual with a bias toward action and a knack for solving "messy" business problems. You will be a systems thinker who enjoys using Python and SQL to deploy predictive models that power YouTube's incentive systems and product activation at scale. Responsibilities Design and build predictive models to power product activation recommendations for YouTube product priorities like shopping, fan funding, podcasts, etc. Generate useful product activation insights and reporting to inform product Go-to-market (GTM) and program strategy. Improve YouTube Business Organization's central scaled incentives system through design levers like pricing, goals, etc. Own problem solving lifecycle from generating hypotheses, analyzing data, distilling insights to communicating recommendations in a structured way. Develop and deploy propensity models using Python, integrating them directly into automated data pipelines to scale predictive insights across the YouTube ecosystem, and navigate YouTube's data warehouse to access and work with relevant data using SQL and other query languages.