Data Science - Global Marketing Engines and Experiences (E&E)

Signal Discovery & Integration Identify and prioritize customer behavior and product data that can improve journey understanding, targeting, and marketing decisions. Profile structured and semi-structured sources and evaluate coverage, identity resolution, quality, bias, latency, stability, and fitness for modeling or activation. Define features, signal taxonomies, normalization methods, and analytical integration patterns; translate validated prototypes into clear requirements for Data Engineering. Journey Intelligence & Activation Develop models and analytical frameworks that map journeys, identify progression and intent, and reveal meaningful engagement patterns across touchpoints. Create and evaluate audience-selection methods, targeting signals, reusable features, and scores for marketing and activation systems. Partner with marketers to translate model outputs into practical journey and activation decisions that are understandable, actionable, and measurable. Measurement & Technical Leadership Design experiments and measurement frameworks to determine whether new signals and interventions improve engagement, progression, marketing efficiency, and business outcomes. Establish reusable standards for source evaluation, validation, explainability, drift monitoring, scientific documentation, and production readiness. Collaborate across data science, engineering, taxonomy, reporting, activation, and marketing teams; communicate findings, uncertainty, and recommendations to technical and executive audiences. Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 6+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science rr related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. Experience discovering, profiling, and analytically integrating behavioral, interaction, product telemetry, web, or event-level data. Proficiency with SQL and Python or R, with experience in modern cloud analytics environments such as Azure, Fabric, or equivalent platforms. Ability to structure ambiguous problems, develop reusable analytical solutions, and communicate effectively with business, engineering, and executive stakeholders. Experience with B2B marketing, customer journeys, audience strategy, digital engagement, account or lead data, or sales-funnel analytics. Experience with web analytics, product usage, trials, skilling, content consumption, intent, or other cross-channel customer behavior signals. Experience building features or models used for targeting, recommendations, journey progression, next-best action, or activation. Knowledge of MLOps practices and privacy, security, and data-protection requirements for customer and behavioral data.