I&F Decision Sci Practitioner Sr Analyst
Core
Design, build, and deploy machine learning models and advanced analytics use cases to power growth analytics strategy for CRM & Loyalty engagement, focusing on audience segmentation and campaign performance.
Role type
Senior Individual Contributor Machine Learning Engineer (Marketing Analytics)
Builds
Production ML models, campaign measurement frameworks, and audience segmentation strategies for global clients.
Domain
Marketing Technology, Customer Experience, Data Science
Deliverable
production ML models
Required skills
Advanced SQL, Python (scikit-learn, XGBoost, LightGBM), classification/regression/ranking modeling, customer analytics methodologies (propensity modeling, CLTV, churn prediction), MLOps practices, CRM/marketing data analysis, experimental design (A/B testing), AI/ML-driven personalization, Power BI/Tableau, segmentation methodologies (RFM, CLV), agentic AI frameworks, LLM-integrated analytics pipelines
Preferred skills
Salesforce Einstein Analytics/Data Cloud, Tealium CDP, automobile/airline/retail/FMCG/beauty industry experience, machine learning or cloud ML platform certifications
Technologies
Python, scikit-learn, XGBoost, LightGBM, SQL, Power BI, Tableau, Salesforce Marketing Cloud, Adobe Campaign, Braze, AWS SageMaker, GCP Vertex AI, Tealium CDP
Responsibilities
Design and build ML models for use cases including propensity scoring, churn prediction, next-best-action modeling, and CLTV prediction; Own the full model lifecycle from problem framing to deployment and monitoring; Partner with data teams to productionize models and integrate outputs into CRM journeys; Design and govern global control group frameworks and statistical methodology; Translate model outputs into actionable audience briefs and campaign recommendations; Conduct CLV, churn, and engagement analyses to identify high-value segments; Lead test-and-learn design (A/B tests) and interpret results; Build and maintain campaign performance dashboards.
Seniority
Senior, hands-on IC