Manager, Marketing Data Science & Predictive Modeling in New York, NY.

Dow Jones and Company, Inc. seeks Manager, Marketing Data Science & Predictive Modeling in New York, NY. Build predictive models to predict customer behavior, such as churn, lifetime value, price sensitivity, channel preference and purchase propensity within the subscriptions businesses. Develop predictive models for upsell and cross sell across the different product lines. Collaborate with data engineering and ML Ops teams to establish and maintain robust data infrastructure, ensuring data quality, accessibility, and security. Establish model governance and implementation processes. Evaluate and implement appropriate data science tools, platforms, and technologies to streamline processes and enhance data-driven marketing capabilities. Utilize advanced analytics and predictive techniques to analyze large volumes of customer data, including demographic information, transactional data, online behavior, and social media data. Derive actionable insights to optimize marketing strategies, improve customer targeting, and personalize marketing campaigns. Work closely with cross-functional teams, including marketing, sales, product, and finance, to understand their business needs and translate them into data-driven solutions. Present insights and recommendations to senior executives and stakeholders in a clear and compelling manner. Requires a Bachelor's degree in Data Science, Statistics, Mathematics, or related field plus 4 years of experience in the following:-Overseeing pricing analytics strategy;-Predictive modeling, machine learning, data mining, forecasting, and predictive analytics;-Programming languages: Python, R, or SQL;-Large-scale databases and data manipulation;-Data visualization and reporting tools: Tableau or Power BI; and-Marketing automation platforms, CRM systems, and digital analytic tools. In the alternative, employer will accept a Master's degree in Data Science, Statistics, Mathematics, or related field plus 1 year of experience in the following:-Overseeing pricing analytics strategy;-Predictive modeling, machine learning, data mining, forecasting, and predictive analytics;-Programming languages: Python, R, or SQL;-Large-scale databases and data manipulation;-Data visualization and reporting tools: Tableau or Power BI; and-Marketing automation platforms, CRM systems, and digital analytic tools.