Senior Data Scientist, Performance Marketing

Senior Data Scientist, Performance Marketing (StubHub, Inc., Santa Monica, CA)

Utilize SQL and programming languages for data science (including Python and R), and numerical and scientific libraries used for statistics and machine learning (including Pandas, NumPy, SciPy, and scikit-learn). Transform and analyze large scale data with modern cloud computing platforms (including SparkSQL, BigQuery, Snowflake, and Redshift). Build interactive reports and reusable analyses with advanced notebook tools (including Hex, Colab, Mode, and Jupyter). Train and build production level models in cloud environments (including AWS and Azure) using orchestration software (including Metaflow and Airflow). Perform time series modeling (including GAMs, GLMs, and ARs) and real-time forecasting. Work with non-technical stakeholders to understand, anticipate, and deliver on their data needs. Own one or more business domains, building deep industry expertise, anticipating the needs of that vertical's leader, and being the data science thought leader. Collaborate cross functionally with engineering, business, and product teams, oftentimes acting as proxy for one or the other. Up to 10% travel required to corporate headquarters located in New York, New York and the company’s other domestic offices in the United States. Telecommuting may be permitted up to 2 days per week. When not telecommuting, must report to StubHub, Inc. at 120 Broadway, 2nd Floor, Santa Monica, CA 90401. Salary: $190,000 to $250,000 per year.

MINIMUM REQUIREMENTS: Bachelor’s degree or U.S. equivalent in Mathematics, Statistics, Computer Science, or a related field, plus 5 years of professional experience as a Data Scientist, Decision Scientist, or any occupation/position/job title involving data science, machine learning, and experimentation.

Must also have experience in the following: 5 years of professional experience utilizing programming languages for data science (including Python and R) and markdown configuration files (including YAML); 5 years of professional experience orchestrating machine learning models using Airflow; 5 years of professional experience building reusable reports and application prototypes for technical and non-technical stakeholders with notebook tools (including Hex); 5 years of professional experience using distributed query services (including Athena and SparkSQL) to transform large data sets; 5 years of professional experience leveraging advanced statistics and scientific libraries (including Pandas, NumPy, SciPy, and scikit-learn) to drive insights for decision-making; 5 years of professional experience building time series models (including GAMs, GLMs, and ARs) and real-time forecasts; 1 year of professional experience leveraging Snowflake SQL and Snowpark to transform and analyze large sets of data; and 1 year of professional experience training and productizing machine learning models using Metaflow.

CONTACT: Apply online

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