Lead Data Scientist

Company Description

VERSANT is a leading force in news, sports and entertainment – home to iconic and trusted brands that inspire, inform, and delight audiences. Our unique combination of content, technology and services enriches the cultural fabric, igniting passions, sparking conversations, and connecting people to what they love most. As an independent, publicly traded company, VERSANT brings together powerhouse cable networks – including USA Network, CNBC, MS NOW (formerly MSNBC), Oxygen, E!,

SYFY, and Golf Channel – with dynamic digital and direct‑to‑consumer brands such as Fandango, Rotten Tomatoes, Golf Now, Golf Pass, and Sports Engine. Together, these businesses reflect our commitment to delivering exceptional experiences across every screen and service. VERSANT is an industry‑changing media company fueled by innovation and an entrepreneurial spirit. With a strong foundation and a forward‑looking vision, VERSANT empowers creativity, embraces change, and drives connection in an ever‑evolving world.

We are looking for a Lead Data Scientist to spearhead data science initiatives focused on media and cable networks. In this role, you will develop and optimize data‑driven engagement and retention models to minimize subscriber churn and accelerate subscription growth. You will collaborate closely with business stakeholders to gather requirements, translate business needs into technical solutions, and clearly communicate insights and recommendations to non‑technical leadership.

Your work will directly influence marketing, content, and pricing strategies to improve customer retention and lifetime value.

• Leverage structured and unstructured data from various media and entertainment sources to prepare datasets for advanced analytics and modeling.
• Develop and deliver impactful analytical tools and solutions leveraging statistical modeling, machine learning, and data science to uncover business insights and support strategic decision‑making.
• Design and apply advanced predictive and machine‑learning models; including clustering (K‑means, hierarchical), classification (KNN, Naive Bayes, CART), time series forecasting, logistic regression, and econometric models to optimize pricing strategies, assess price elasticity, segment customers, and enhance revenue across channels.
• Leverage generative AI and large language models (LLMs) to develop and implement personalized content and messaging strategies across diverse media channels, enhancing audience engagement and campaign effectiveness.
• Assess and validate statistical models using appropriate performance metrics to ensure precision and accuracy such as accuracy, sensitivity, specificity, ROC, AUC.
• Analyze consumer behavior trends and shifts across various digital touchpoints; perform cross‑channel attribution analysis to inform targeted retention strategies.
• Monitor and analyze key engagement metrics to assess the performance of subscriber onboarding programs and their impact on long‑term retention.
• Interpret complex analytical insights and translate them into clear, actionable business strategies that improve business outcomes.
• Support scalable deployment of data products by following best practices in CI/CD processes and contribute to agile project management through tools like Jira for sprint planning, tracking, and team coordination.
• Collaborate cross‑functionally with technical and non‑technical stakeholders to gather requirements, define project scope, and lead data science initiatives, demonstrating strong communication, leadership, and team‑building skills.

• Master’s or PhD in data Science, statistics, computer science, or related quantitative field
• 5+ years’ experience in data science roles with demonstrated impact on retention, engagement, or churn reduction
• Advanced skills in Python/R, SQL, and experience with ML libraries (scikit‑learn, XGBoost, Tensor Flow/PyTorch)
• Strong background in building predictive churn models, CLV, causal inference and uplift modeling
• Leverage cloud platforms (e.g., AWS, Azure) and big data tools (e.g., Spark, Hive, Databricks), staying current with evolving technologies and Databricks’…