Data Science, Global Marketing Engines and Experiences

What you'll do

• Identify and prioritize customer behavior and product data to improve journey understanding and marketing decisions.
• Profile data sources and evaluate coverage, identity resolution, quality, bias, and fitness for modeling.
• Define signal taxonomies, normalization methods, and analytical integration patterns for Data Engineering requirements.
• Develop models and frameworks to map customer journeys and identify progression and intent across touchpoints.
• Create audience-selection methods, targeting signals, and reusable features for marketing and activation systems.
• Design experiments and measurement frameworks to evaluate the impact of new signals and interventions.
• Establish standards for source evaluation, validation, explainability, drift monitoring, and production readiness.
• Translate model outputs into actionable journey and activation decisions for marketing teams.

What we're looking for

• Obtain a Doctorate and 1+ years of experience, a Master's and 3+ years, or a Bachelor's and 5+ years in data science.
• Alternatively, obtain a Doctorate and 3+ years of experience, a Master's and 6+ years, or a Bachelor's and 8+ years in data science.
• Experience discovering, profiling, and analytically integrating behavioral, interaction, product telemetry, web, or event-level data.
• Proficiency with SQL and Python or R in modern cloud analytics environments such as Azure, Fabric, or equivalent platforms.
• 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.