Data Science Lead (TV Subscriptions, App Personalization, Python)
Note - If shortlisted, we’ll contact you via WhatsApp and email. Please check both and respond promptly.
• Candidates must be Green Card holders or U.S. citizens.
• Open to candidates willing to relocate to New York (Relocation assistance: $6K).
• Work setup: 5 days in office.
• Open to full-time or contract positions - Salary USD 120000 - 149000($95/hr for contractors).
Job Description
• We are seeking a Senior Data Scientist to drive predictive modeling, personalization, and analytics initiatives aimed at boosting ticket sales, optimizing TV subscriptions, and enhancing fan engagement across digital platforms. The role requires strong expertise in Python, SQL, and machine learning, along with experience in handling complex datasets and deploying models in production environments.
Key Responsibilities
• Predictive Modeling & Analytics: Develop and maintain models for fan lifetime value (LTV), ticketing, TV subscriptions, and e-commerce transactions.
• Ticketing Optimization: Analyze purchasing behaviors to encourage single-game buyers to opt for multi-game packages and improve targeted marketing efforts.
• Subscription & Churn Analysis: Create predictive models to assess TV subscription renewals, churn risks, and engagement trends.
• App Personalization: Enhance app experience through personalized push notifications and engagement strategies.
• Data Integration & Feature Engineering: Work with data engineers to integrate multiple data sources and develop a comprehensive fan profile.
• Model Deployment & ML Operations: Deploy and manage machine learning models using platforms such as Dataiku and GCP to ensure scalability and efficiency.
• A/B Testing & Experimentation: Implement and analyze A/B testing using internal tools and Adobe Analytics.
Qualifications
• Proficiency in Python and SQL for data analysis and model development.
• Strong background in machine learning, predictive analytics, and statistical modeling.
• Experience with Dataiku, Google Cloud Platform (GCP), and ML deployment pipelines.
• Ability to work with messy datasets and build effective feature engineering solutions.
• Expertise in A/B testing methodologies and data-driven decision-making.
• Strong problem-solving skills and ability to collaborate with cross-functional stakeholders.
Preferred Qualifications
• Experience in ticketing analytics, subscription-based modeling, or e-commerce data.
• Familiarity with Argo CD for managing ML models in cloud environments.
• Background in sports analytics or digital engagement strategies.
• Minimum 5 years of experience in Machine Learning and Deep Learning.
• Minimum 10 years of experience in Python and SQL.
Note - If shortlisted, we’ll contact you via WhatsApp and email. Please check both and respond promptly.
• Candidates must be Green Card holders or U.S. citizens.
• Open to candidates willing to relocate to New York (Relocation assistance: $6K).
• Work setup: 5 days in office.
• Open to full-time or contract positions - Salary USD 120000 - 149000($95/hr for contractors).
Job Description
• We are seeking a Senior Data Scientist to drive predictive modeling, personalization, and analytics initiatives aimed at boosting ticket sales, optimizing TV subscriptions, and enhancing fan engagement across digital platforms. The role requires strong expertise in Python, SQL, and machine learning, along with experience in handling complex datasets and deploying models in production environments.
Key Responsibilities
• Predictive Modeling & Analytics: Develop and maintain models for fan lifetime value (LTV), ticketing, TV subscriptions, and e-commerce transactions.
• Ticketing Optimization: Analyze purchasing behaviors to encourage single-game buyers to opt for multi-game packages and improve targeted marketing efforts.
• Subscription & Churn Analysis: Create predictive models to assess TV subscription renewals, churn risks, and engagement trends.
• App Personalization: Enhance app experience through personalized push notifications and engagement strategies.
• Data Integration & Feature Engineering: Work with data engineers to integrate multiple data sources and develop a comprehensive fan profile.
• Model Deployment & ML Operations: Deploy and manage machine learning models using platforms such as Dataiku and GCP to ensure scalability and efficiency.
• A/B Testing & Experimentation: Implement and analyze A/B testing using internal tools and Adobe Analytics.
Qualifications
• Proficiency in Python and SQL for data analysis and model development.
• Strong background in machine learning, predictive analytics, and statistical modeling.
• Experience with Dataiku, Google Cloud Platform (GCP), and ML deployment pipelines.
• Ability to work with messy datasets and build effective feature engineering solutions.
• Expertise in A/B testing methodologies and data-driven decision-making.
• Strong problem-solving skills and ability to collaborate with cross-functional stakeholders.
Preferred Qualifications
• Experience in ticketing analytics, subscription-based modeling, or e-commerce data.
• Familiarity with Argo CD for managing ML models in cloud environments.
• Background in sports analytics or digital engagement strategies.
• Minimum 5 years of experience in Machine Learning and Deep Learning.
• Minimum 10 years of experience in Python and SQL.
