Senior Data Scientist

We're on the lookout for an accomplished Senior Data Scientist to join the Data Engineering & Analytics team at StubHub. In this role, you'll play a pivotal part in crafting and deploying cutting-edge machine learning models that drive business and operational choices within StubHub's dynamic ecosystem. 
  
Our Data Science team serves as the backbone for enabling data-driven decisions across the organization, harnessing the potential of StubHub's consumer data through statistical and machine learning techniques. This involves creating both business-facing and customer-facing products, along with delivering user-friendly tools and clear explanations for non-technical users to enhance self-service capabilities. 

• Build and understand modeling related to one of four possible areas at StubHub: Experimentation, Forecasting, Pricing or Customer Acquisition
• Build state-of-the-art machine learning models for which you own from design all the way through to production, and communicate your results to broad technical and non-technical audiences
• Serve as a subject matter expert to the broader organization around on how to best build predictive modeling in the context of forecasting and pricing
• Find and develop your own roadmaps with business partners and carry out project management and execution of your own work
• Drive growth of junior team members and the data organization through venues such as workshops, best practices, and reading groups
• Foster a culture of inclusion, results-oriented execution, open innovation, and limitless creativity across your team 

• →7-11 years of relevant data science or machine learning experience and an MS. or Ph.D. degree in math, statistics, computer science, or other quantitative fields preferred.
• →Proficiency with transforming and analyzing large scale data with modern cloud computing platforms (e.g.,SparkSQL, BigQuery, Snowflake, Databricks), with high proficiency with query languages like SQL
• →Expert in Python or R and numerical and scientific libraries used for statistics and machine learning (e.g., Pandas, NumPy, SciPy, scikit-learn)
• →Experience in building production level models in cloud environments (e.g., AWS, Azure)
• →Demonstrated proficiency in time series modeling (GAMs, GLMs, ARs) and real-time forecasting are a nice-to-have
• →Passionate about working with non-technical stakeholders to understand, anticipate, and deliver on their data needs