Duties: Data Scientist will be responsible for leading key data science and analytics initiatives in support of marketing and ticket sales/renewal strategy. Apply statistical rigor to the organization’s business questions and take ownership of the creation of predictive and machine learning models and the resulting insights from those projects.

Lead analytics projects from start to finish, utilizing the full data science skillset including R and/or Python, SQL and visualization tools;
Create retention and cross-sell models to reduce customer “churn” and maximize revenue among ticket plan holders;
Develop propensity models to prioritize ticket sales efforts;
Quantitatively analyze data to create fan segmentations and collaborate on marketing strategies to drive engagement and spending among those segmentations;
Aid in the experiment design and optimization of marketing campaigns, in addition to identifying the optimal marketing mix for advertising spend;
Quantitatively determine optimal pricing for tickets, concessions and upgrades;
Apply statistical and analytical rigor to analyze datasets and drive data-backed decisions.

Requirements:

Bachelor’s in Mathematics, Statistics, Computer Science, Data Science, Economics, Financial Analysis, Business Analysis, or a related quantitative discipline. Three years of professional experience, including: Managing a team of 2 or more direct reports, acting as project manager in a strategy consulting setting, responsible for strategy design, execution, and evaluation, Managing multiple projects, with diverse stakeholders, simultaneously. Must have 12 months of data analytics experience, including: Working with big data, in structured and unstructured formats, Conducting end-to-end machine learning projects that include all 6 stages of the data mining roadmap: problem definition, exploratory analysis, feature engineering, model training, model evaluation and model implementation, Working with Python, R, Tableau, SQL and Microsoft Office, Using data analysis and statistical learning programming libraries, including Pandas, Tidyverse and Scikit-learn, Leveraging applied statistics, statistical learning techniques, econometrics, and data visualization techniques, and Proficiency in text mining and natural language processing techniques including: sentiment analysis, topic modelling, and semantic network analysis Full-time.