Data Scientist
Location: New York (3 days work from office)
About the Role: We are seeking a skilled and experienced Data Scientist to join our team and contribute to a range of data science initiatives focused on understanding and engaging Sport fans. This role involves working on predictive modeling, personalization, and analytics to enhance ticket sales, optimize Client TV subscriptions, and improve the overall fan experience across digital platforms. The ideal candidate will have expertise in Python, SQL, and machine learning, along with experience in handling complex datasets and deploying models in production.
Key Responsibilities
• Predictive Modeling & Analytics: Develop and maintain models for fan lifetime value (LTV), ticketing, TV subscriptions, and Shop purchases.
• Ticketing Optimization: Build models to analyze purchasing behaviors, drive single-game buyers toward multi-game packages, and enhance targeted marketing strategies.
• Subscription & Churn Analysis: Develop predictive models to assess TV subscription renewals, churn risk, and engagement trends.
• App Personalization: Work on personalizing the app experience through push notifications and engagement strategies.
• Data Integration & Feature Engineering: Collaborate with data engineers to integrate multiple data sources and create a holistic fan profile.
• Model Deployment & ML Operations: Deploy and manage machine learning models using Dataiku and GCP, ensuring scalability and performance.
• A/B Testing & Experimentation: Utilize in-house solutions and Adobe Analytics to implement and analyze A/B testing for various initiatives.
Qualifications
• Proficiency in Python and SQL for data analysis and model development.
• Experience with machine learning, predictive analytics, and statistical modeling.
• Familiarity with Dataiku, AWS, Azure, GCP (Google Cloud Platform), and ML deployment pipelines.
• Ability to work with messy datasets and develop efficient feature engineering solutions.
• Strong analytical and problem-solving skills with experience in A/B testing methodologies.
• Excellent communication skills and the ability to collaborate with stakeholders across revenue streams.
Preferred Qualifications
• Experience in ticketing analytics, subscription-based modeling, or e-commerce data.
• Knowledge of Argo CD for managing ML models in cloud environments.
• Background in sports analytics or digital engagement strategies.
Team & Work Environment
• Work as part of a full-stack data science team, handling model building, deployment, and maintenance.
• Collaborate with analysts, data engineers, and business stakeholders to drive data-driven decision-making.
• Opportunity to shape the future of fan engagement through innovative data science solutions.
Location: New York (3 days work from office)
About the Role: We are seeking a skilled and experienced Data Scientist to join our team and contribute to a range of data science initiatives focused on understanding and engaging Sport fans. This role involves working on predictive modeling, personalization, and analytics to enhance ticket sales, optimize Client TV subscriptions, and improve the overall fan experience across digital platforms. The ideal candidate will have expertise in Python, SQL, and machine learning, along with experience in handling complex datasets and deploying models in production.
Key Responsibilities
• Predictive Modeling & Analytics: Develop and maintain models for fan lifetime value (LTV), ticketing, TV subscriptions, and Shop purchases.
• Ticketing Optimization: Build models to analyze purchasing behaviors, drive single-game buyers toward multi-game packages, and enhance targeted marketing strategies.
• Subscription & Churn Analysis: Develop predictive models to assess TV subscription renewals, churn risk, and engagement trends.
• App Personalization: Work on personalizing the app experience through push notifications and engagement strategies.
• Data Integration & Feature Engineering: Collaborate with data engineers to integrate multiple data sources and create a holistic fan profile.
• Model Deployment & ML Operations: Deploy and manage machine learning models using Dataiku and GCP, ensuring scalability and performance.
• A/B Testing & Experimentation: Utilize in-house solutions and Adobe Analytics to implement and analyze A/B testing for various initiatives.
Qualifications
• Proficiency in Python and SQL for data analysis and model development.
• Experience with machine learning, predictive analytics, and statistical modeling.
• Familiarity with Dataiku, AWS, Azure, GCP (Google Cloud Platform), and ML deployment pipelines.
• Ability to work with messy datasets and develop efficient feature engineering solutions.
• Strong analytical and problem-solving skills with experience in A/B testing methodologies.
• Excellent communication skills and the ability to collaborate with stakeholders across revenue streams.
Preferred Qualifications
• Experience in ticketing analytics, subscription-based modeling, or e-commerce data.
• Knowledge of Argo CD for managing ML models in cloud environments.
• Background in sports analytics or digital engagement strategies.
Team & Work Environment
• Work as part of a full-stack data science team, handling model building, deployment, and maintenance.
• Collaborate with analysts, data engineers, and business stakeholders to drive data-driven decision-making.
• Opportunity to shape the future of fan engagement through innovative data science solutions.
